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Mistreatment in Childbirth: A mixed-methods approach to understand the mental health sequelae of mistreatment in maternity care among a diverse cohort of birthing persons in New York City
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The present study aimed to explore the objective and subjective experiences of “mistreatment” in maternity care in a diverse cohort of women who gave birth in New York City hospitals to identify the prevalence and risk factors of mistreatment and measure the relationship between mistreatment and mental health (Bohren et al., 2015). The study utilized a mixed-methods cross-sectional approach. To collect the quantitative data, 109 participants <1 year postpartum completed an anonymous online survey comprising a self-report measure of demographic, health and mental health information, several mental health questionnaires and two measures of mistreatment in maternity care. 8 of these participants were interviewed about their childbirth experience. The quantitative data was analyzed utilizing linear regression, moderation analysis and path analysis, and the qualitative data was thematically coded then analyzed using Reflexive Thematic (RT) analysis. These data were then triangulated using a mixed-methods model of mistreatment. In total, 10-15% of the sample experienced mistreatment in the form of Low to Very Low respect and/or autonomy in decision making in their maternity care. Forms of mistreatment included unwanted procedures, provider pressure to undergo procedures, dismissal of women’s concerns, racial discrimination, abandonment, and medical neglect. Approximately 25% of respondents received an unwanted intervention; this was the most significant predictor of mistreatment. This relationship was moderated by race, parity and birth plan. Black, Latinx and Hispanic women experienced the lowest levels of respect in maternity care. Mistreatment in maternity care was correlated with increased risk for postpartum mental illness: decreased respect and autonomy in childbirth was associated with increased postpartum depression and PTSD symptoms. Eight themes were identified in the qualitative analysis: Discrimination and Unfair Treatment, Confusion and Abandonment, Disregard for Patient Autonomy, Hospital-Level Drivers of Mistreatment, Women Treated as Passive, Normalization of Mistreatment, Self-Advocacy and Vulnerability and, Reclaiming Power through Knowledge. Together, the triangulated mixed- methods data were fit to render a comprehensive “model of mistreatment” to illustrate direct and indirect relationships between mistreatment, mental health, race, trauma history, and childbirth preparation. These findings demonstrate that mistreatment is a multi-determined phenomenon that is interdependent with mental health and requires systematic measurement in healthcare treatment, the integration of anti-racist and patient-centered care and improved childbirth education for patients
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Mistreatment in Childbirth: A mixed-methods approach to understand the mental health sequelae of mistreatment in maternity care among a diverse cohort of birthing persons in New York City Anika F. Alix Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Clinical Psychology under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2024 © 2023 Anika F. Alix All Rights Reserved - - i ABSTRACT Mistreatment in Childbirth: A mixed-methods approach to understand the mental health sequelae of mistreatment in maternity care among a diverse cohort of birthing persons in New York City Anika F. Alix The present study aimed to explore the objective and subjective experiences of “mistreatment” in maternity care in a diverse cohort of women who gave birth in New York City hospitals to identify the prevalence and risk factors of mistreatment and measure the relationship between mistreatment and mental health (Bohren et al., 2015). The study utilized a mixed-methods cross-sectional approach. To collect the quantitative data, 109 participants <1 year postpartum completed an anonymous online survey comprising a self-report measure of demographic, health and mental health information, several mental health questionnaires and two measures of mistreatment in maternity care. 8 of these participants were interviewed about their childbirth experience. The quantitative data was analyzed utilizing linear regression, moderation analysis and path analysis, and the qualitative data was thematically coded then analyzed using Reflexive Thematic (RT) analysis. These data were then triangulated using a mixed-methods model of mistreatment. In total, 10-15% of the sample experienced mistreatment in the form of Low to Very Low respect and/or autonomy in decision making in their maternity care. Forms of mistreatment included unwanted procedures, provider pressure to undergo procedures, dismissal of women’s concerns, racial discrimination, abandonment, and medical neglect. Approximately 25% of respondents received an unwanted intervention; this was the most significant predictor of mistreatment. This relationship was moderated by race, parity and birth plan. Black, Latinx and - - ii Hispanic women experienced the lowest levels of respect in maternity care. Mistreatment in maternity care was correlated with increased risk for postpartum mental illness: decreased respect and autonomy in childbirth was associated with increased postpartum depression and PTSD symptoms. Eight themes were identified in the qualitative analysis: Discrimination and Unfair Treatment, Confusion and Abandonment, Disregard for Patient Autonomy, Hospital-Level Drivers of Mistreatment, Women Treated as Passive, Normalization of Mistreatment, Self-Advocacy and Vulnerability and, Reclaiming Power through Knowledge. Together, the triangulated mixed-methods data were fit to render a comprehensive “model of mistreatment” to illustrate direct and indirect relationships between mistreatment, mental health, race, trauma history, and childbirth preparation. These findings demonstrate that mistreatment is a multi-determined phenomenon that is interdependent with mental health and requires systematic measurement in healthcare treatment, the integration of anti-racist and patient-centered care and improved childbirth education for patients. - - iii TABLE OF CONTENTS LIST OF TABLES………………………………………………………………………...……..vii LIST OF FIGURES ……………………………………………………………………….….….xi ABBREVIATIONS…………………………………………………………………………..….xii ACKNOWLEDGEMENTS………………………………………………………………..……xiii DEDICATION……………………………………………………………………………….….xiv CHAPTER I: INTRODUCTION………………………………………………………….……...1 CHAPTER II: BACKGROUND AND LITERATURE REVIEW…………………………...…..2 Becoming a Mother, Birthing a Mother…………………………………………………………..2 Childbirth Experience……………………………………………………………………………..5 Factors that Impact Childbirth Experience………………………………………………..5 Mistreatment in Labor…………………………………………………………………………….6 Examples of Mistreatment…………………………………………………………….…..7 Mistreatment: Defining a Phenomenon………………………………………….………11 Mistreatment by Region………………………………………………………………….13 Possible Impacts of Mistreatment in Labor…………………………………………………...…14 Mental Health Implications………………………………………………………………15 Mother-Child Relationship………………………………………………………………18 Giving Birth in America: Mortality and Morbidity……………………………………………...19 Maternal Mortality and Severe Maternal Morbidity…………………………………..…19 Reducing Mistreatment as an Avenue for Reducing Mortality……………………….…19 Focus on: New York City…………………………………………………………..……20 Risk Factors and Mistreatment in American Childbirth…………………………………………21 - - iv Race……………………………………………………………………………………...21 Social and Economic Risk Factors………………………………………………………23 Access……………………………………………………………………………………24 Medical Complications: “High-Risk” Pregnancies…………………….…………….….25 COVID-19……………………………………………………………………………….28 Protective Factors for Mistreatment in American Childbirth……………………………………28 Childbirth Education…………………………………………………………..…………28 Drivers of Mistreatment in America…………………………………………………..…………30 American Medical Culture : “Too Much Too Soon” ……………………………………30 Global Mental Health Lens : Paradox of Maternal Mistreatment in America………...…32 Measuring Mistreatment…………………………………………………………………………38 Valid Measures of Mistreatment: MORi and MADM…………………………...………38 CHAPTER III: CURRENT STUDY……………………………………………………….……40 Significance of this work…………………………………………………………………...……41 Aims and Hypotheses……………………………………………………………………41 CHAPTER IV: METHOD………………………………………………………………………46 Setting and Participants…………………………………………………………..……………...46 Data Collection………………………………………………………………………………..…47 Procedure………………………………………………………………………………...………48 Measures…………………………………………………………………………………………48 Mothers on Respect Index (MORi)…………….…………………………..……….…...49 Mother’s Autonomy in Decision Making Scale (MADM)…………………..…..…...….51 Perceived Racism Scale (PRS)………………………………………….………….……52 - - v Patient Health Questionnaire (PHQ-9)…………………..………………………………52 PTSD Checklist for DSM-5 (PCL-5)……………………………………………………53 Adverse Childhood Experiences Scale (ACE)...………………………………………...54 Mother Infant Bonding Scale (MIBS)…………………………….…………………..…55 Establishing Validity of Online-Administered Measures………………………………………..55 Detection of “bots” and fraudulent responses……………………………………………56 Data Analysis……………………………………………………………………………….……57 CHAPTER V: RESULTS……………………………………………………………………..…71 Quantitative Results………………………………………………………………………...……71 Recruitment & Descriptive Statistics…………………………………………………….71 Prevalence, Frequency, and Forms of Mistreatment and Mental Health……………...…79 Risk and Protective Factors of Mistreatment ………………………………………..…..85 Relationship Between Mistreatment and Mental Health………………………………...98 Path Analysis: “Model of Mistreatment”…………………………………………….…105 Qualitative Results……………………………………………………………………………...112 Participants & Descriptive Statistics……………………………………………………112 Final Codebook…………………………………………………………………………117 Thematic Outcomes………………………………………………………………….…119 Triangulation of Mixed Methods Results……………………………………………………....132 CHAPTER VI: DISCUSSION…………………………………………………………………137 Main Findings………………………………………………………………………………..…137 Mistreatment in Maternity Care……………………………………………………...…137 Risk and Protective Factors for Mistreatment………………………………..…………138 - - vi Relationship between Maternal Mistreatment and Maternal Mental Health…………...144 Provider Role in Preventing and Engendering Mistreatment………………………..…147 Normalization of Mistreatment…………………………………………………………148 New York City Risk Factors: Birthing in a Different Borough…………………...……149 Main Findings in Context: American Medicine…………………………………………..……151 Right to Refuse Care and “Capacity” ………………………………….………………151 Maternal Health and Women’s Rights: Reproductive Justice…………….……………153 Advocating for My Own Rights: A Double-Edged Sword…………………………..…155 Barriers to Improving Hospital Births in America…………………………………..…156 Clinical Implications……………………………………………………………………………158 Mental Health Practitioners……………………………………………………….……159 Medical Practitioners and Health Services…………………………………………..…160 Research Implications………………………………………………………………………..…164 Limitations & Future Directions……………………………………………………………..…166 Recruitment Method……………………………………………………………………166 Representation………………………………………………………………..…………166 Online Data Collection………………………………………………………..………..167 COVID-19………………………………………………………………………………168 Future Directions……………………………………………………….……………….168 CHAPTER VII: CONCLUSION…………………………………………………………….…170 A Call to Action……………………………………………………...…………………174 APPENDICES A-I…………………………………………………………...…………………175 REFERENCES…………………………………………………………………………………203 - - vii LIST OF TABLES Table 1. Descriptive Statistics: Demographic Means……………………………………………71 Table 2. Frequency Table for Race………………………………………………………………73 Table 3. Frequency Table for Religion………………………………………………………..…73 Table 4. Descriptive Statistics: Demographic Frequencies……………………………………..74 Table 5. Descriptive Statistics: Mental Health Frequencies…………………………………….75 Table 6. Descriptive Statistics: Birth and Health Variable Frequencies………………………..77 Table 7. Descriptive Statistics: Gestational and Infant Health Frequencies……………………78 Table 8. Summary Statistics Table for MADM and MORi Scores………………………………79 Table 9. Summary Statistics Table for Mental Health Variables of Interest………………….....82 Table 10. MANOVA Results for MADM Total and MORI Total (by Race, Health Insurance, Birth Plan, C- section Emergency, Unwanted Interventions, Parity, and Birth Companion)…87 Table 11. Analysis of Variance Table for MADM Total (by Race, Health Insurance, Birth Plan, C- section Emergency, Unwanted Interventions, Parity, and Birth Companion)……….88 Table 12. Analysis of Variance Table for MORI Total (by Race, Health Insurance, Birth Plan, C- section Emergency, Unwanted Interventions, Parity, and Birth Companion)…………...90 Table 13. Moderation Analysis Table with MORI_Total Predicted by Race_Scale Moderated by Diabetes…………………………………………………………Appendix I Table 14. Moderation Analysis Table with MORI_Total Predicted by Race_Scale Moderated by Hypertension……………………………………………………………………Appendix I Table 15. Moderation Analysis Table with MORI_Total Predicted by Race_Scale Moderated by Overweight……………………………………………………...Appendix I Table 16. Moderation Analysis Table with MORI_Total Predicted by Race_Scale Moderated by PR_Total………………………………………………………..Appendix I Table 17. Moderation Analysis Table with MORI_Total Predicted by C_section_Emergency Moderated by Childbirth_Class_Lifetime………………………………………Appendix I Table 18. Moderation Analysis Table with MORI_Total Predicted by Unwanted_Interventions Moderated by Birth_Plan………………………………………………………Appendix I - - viii Table 19. Moderation Analysis Table with MORI_Total Predicted by Unwanted_Interventions Moderated by Caucasian Yes/No………………………………………………Appendix I Table 20. Moderation Analysis Table with MORI_Total Predicted by Unwanted_Interventions Moderated by Childbirth_Class_Lifetime……………………………………...Appendix I Table 21. Moderation Analysis Table with MADM_Total Predicted by Unwanted_Interventions Moderated by Birth_Plan………………………………………………………………...95 Table 22. Simple slopes analysis for Birth_Plan moderating the relationship between Unwanted_Interventions and MADM_Total……………………………………………..96 Table 23. Moderation Analysis Table with MADM_Total Predicted by Unwanted_Interventions Moderated by Caucasian Yes/No………………………………………………………...96 Table 24. Simple Slopes Analysis for Caucasian Yes/No Moderating the relationship between Unwanted Interventions and MADM total…………………………………………….....98 Table 25. Moderation Analysis Table with MADM_Total Predicted by Unwanted_Interventions Moderated by Childbirth_Class_Lifetime……………………………………...Appendix I Table 26. Moderation Analysis Table with MADM_Total Predicted by C_section_Emergency Moderated by Childbirth_Class_Lifetime……………………………………...Appendix I Table 27. Spearman Correlation Results Among MORI_Total, MADM_Total, and Mental Health Measures Total Scores…………………………………………………………………...99 Table 28. Moderation Analysis Table with PHQ-9 Predicted by Mental_Illness_Hx Moderated by MORI Total………………………………………………………………….Appendix I Table 29. Moderation Analysis Table with PHQ-9 Predicted by Mental_Illness_Hx Moderated by MADM_Levels………………………………………………………………Appendix I Table 30. Moderation Analysis Table with PCL-5 Predicted by Mental_Illness_Hx Moderated by MADM_Levels………………………………………………………………….Appendix I Table 31. Moderation Analysis Table with PCL5_Total Predicted by Mental_Illness_History Moderated by MORI……………………………………………………………Appendix I Table 32. Moderation Analysis Table with Suicidal Sx Predicted by Mental_Illness_Hx Moderated by MORI_Total……………………………………………………..Appendix I Table 33. Moderation Analysis Table with PHQ-9 Predicted by Birth_Plan Moderated by MADM_Levels………………………………………………………………….Appendix I - - ix Table 34. Moderation Analysis Table with MORI_Total Predicted by Birth Plan Moderated by PHQ_Total……………………………………………………………………...Appendix I Table 35. Moderation Analysis Table with PCL-5 Predicted by Birth_Plan Moderated by MADM_Levels………………………………………………………………………….103 Table 36. Moderation Analysis Table with MORI_Total Predicted by C_section_Emergency Moderated by PHQ_Total……………………………………………………...Appendix I Table 37. Moderation Analysis Table with PHQ_Total Predicted by C_section_Emergency Moderated by MADM_Levels…………………………………………………..Appendix I Table 38. Moderation Analysis Table with PHQ_Total Predicted by NICU Moderated by MADM_Total…………………………………………………………………...Appendix I Table 39. Moderation Analysis Table with MORI_Total Predicted by NICU Moderated by PHQ_Total……………………………………………………………………...Appendix I Table 40. Moderation Analysis Table with MORI_Total Predicted by NICU Moderated by PCL5_Total……………………………………………………………………..Appendix I Table 41. Moderation Analysis Table with PCL_5 Total Predicted by NICU Moderated by MADM_Total…………………………………………………………………...Appendix I Table 42. Moderation Analysis Table with MORI_Total Predicted by C_section_Emergency Moderated by PCL5_Total……………………………………………………..Appendix I Table 43. Moderation Analysis Table with PCL5_Total Predicted by C_section_Emergency Scale Moderated by MADM_Levels……………………………………………Appendix I Table 44. Unstandardized Loadings (Standard Errors), Standardized Loadings, and Significance Levels for Each Parameter in the path analysis Model (N = 99) ……………………...106 Table 45. Fit Indices for the path analysis model………………………………………………107 Table 46. Estimated Error Variances and R2 Values for Each Endogenous Variable in the SEM model. …………………………………………………………………………………..108 Table 47. Descriptive Statistics of Interview Data: Demographic Means……………………..113 Table 48. Interview Descriptive Statistics: Demographic Frequencies………………………..114 Table 49. Descriptive Statistics of Interview Data: Perinatal Variables………………………115 Table 50, Summary Statistics Table for Interview MADM and MORi Scores…………………116 - - x Table 51. Summary Statistics Table for Interview Mental Health Variables of Interest………116 Table 52. Summary Table for Total Cohen’s Kappa Intercoder Reliability (ICR)……….……117 . Table 53. Intercoder Reliability (ICR) Cohen’s Kappa Final Measures by Coding Group…...119 - - xi LIST OF FIGURES Figure 1. The procedure employed for coding, re-coding and establishing codebook frame…...66 Figure 2. Histogram of total MADM scores, where 42 is the maximum highest score.…………81 Figure 3. Histogram of total MORI scores, where 84 is the maximum highest score.…………..81 Figure 4. Means of MADM_Total by Unwanted_Interventions with 95.00% CI Error Bars…...88 Figure 5. Means of MORI_Total by Race with 95.00% CI………………………………………89 Figure 6. Means of MORI_Total by Unwanted_Interventions with 95.00% CI……………..…..89 Figure 7. Means of MORI_Total by Primaparous with 95.00% CI Error Bars……………..…..90 Figure 8. Regression lines for MADM_Total predicted by Unwanted_Interventions for each category of Birth_Plan…………………………………………………………………...94 Figure 9. Regression lines for MADM_Total predicted by Unwanted_Interventions for the High and Low categories of Caucasian Yes/No…………………………...……..97 Figure 10. Regression lines for PCL5_Total predicted by Birth_Plan for each category of MADM_Levels………………………………………………………………………….103 Figure 11. Node diagram for the path analysis with key……………………………………….109 Figure 12. Thematic map showing the eight main themes and sub-themes……………….……120 Figure 13. Triangulated quantitative and qualitative results rendering a mixed-data and mixed- method Model of Mistreatment…………………………………………………………133 - - xii ABBREVIATIONS ACE: Adverse Childhood Experiences Scale BDI: Beck Depression Inventory CB-PTSD: Posttraumatic Stress Disorder related to recent childbirth EPDS: Edinburgh Postpartum Depression Scale LBW: Low Birth Weight L&D: Labor and Delivery MADM: Mother’s Autonomy in Decision-Making Scale MIBS: Mother Infant Bonding Scale MMR: Maternal Mortality Rate MORi: Mothers on Respect Index PAR: Participatory Action Research PCL-5: PTSD Checklist for DSM-5 PHQ-9: Patient Health Questionnaire, 9-item PPD: Postpartum Depression PRS: Perceived Racism Scale PTSD: Posttraumatic Stress Disorder RMC: Respectful Maternity Care RT: Reflexive Thematic Analysis SMM: Severe Maternal Morbidity TA: Thematic Analysis TIC: Trauma-Informed Care UHDVA: Unintentional Harm, Disrespect, Violence, and Abuse (in labor) - - xiii ACKNOWLEDGEMENTS This work was made possible by the generosity and support of people I have had the honor of working with at Teachers College, Columbia University. I owe my deepest gratitude to my mentor, Dr. Lena Verdeli. You turned to look at me from the front seat of a 4 x 4 in rural Uganda and, through the red clay dust in the air, said, “So, tell me about your interest in mothers,” and the rest is history. You are the smartest person in any room and possess infinite creativity; you have the mind of an analyst and the heart of a saint—lucky and blessed are those who have a place in your lab and your heart. I am honored by your mentorship these eight years. The design, methodology, data collection and analysis for the CNYC study were supported by Dr. Lena Verdeli, Dr. Bryan Cheng, and the Global Mental Health Lab. Dr. Bryan Cheng generously provided me with unlimited advisement and created scaffolding to help me grow as a statistician. I am grateful for the formative mentorship of Dr. Alaa Alhomaizi; she gave me my first “shot” in our lab as a young master’s student, trained me in qualitative coding and opened my eyes to the rich academic world of postpartum depression. To Dr. Kati Lake—my colleague, co-therapist, ally, and best friend: you helped me to believe in me when I did not. Thank you for reminding me that “even a blind squirrel finds a nut sometimes;” I think I found my nut. Thank you to my outstanding CNYC study project coordinator, Caroline Lovett and my two dedicated research assistants, Emily Dunkel and Runqin Shi. This work would not be possible without you three. I am grateful to my wonderful committee chair, Dr. Barry Farber, and his fellow committee members, Dr. Lena Verdeli, Dr. Prerna Arora, Dr. Bryan Cheng, and Dr. Claire Greene for their support in seeing this study through from its nascence. Thank you as well to the Teachers College IRB. Finally, I want to acknowledge the participants of the CNYC study who generously took the time to share their childbirth experiences with me. It is an honor to share your story. - - xiv DEDICATION It is with my sincerest heartfelt gratitude that I dedicate this work to my patients, my teachers and my family. In my time as a therapist on psychiatric inpatient units, I had the opportunity to support women experiencing perinatal mental health disorders. Through this work I learned to navigate the rocky road to motherhood alongside them. The shockingly poor treatment many of these women received in their childbirth experience moved me to action; the dearth of perinatal mental health treaters in the city further lit my flame. This work is dedicated to the women who have suffered unnecessarily due to misdiagnosis, systemic-oppression, and a psychiatric field that is still learning to support them without old fears creeping in. I chose this research path in dedication to each of my extraordinary patients whose strengths and struggles imprinted this mission upon me. I am grateful to a lifetime of teachers, mentors and supervisors who invested in my education and fostered my love of learning. Thank you to Detroit Country Day School for creating a rich learning environment where a curious child like me could thrive. Thank you to my French professors at the University of Miami who encouraged me to obtain higher education in psychology—what could be more French than la magnifique tristesse? Most of all, thank you to the unparalleled professors at Teachers College. Dr. Lena Verdeli, you gave me untold opportunity as both a clinician and a researcher; you equipped me with the skills I needed, the confidence I lacked and gave me the wings to take flight. This work is a mirror of your mentorship. Dr. Aurelie Athan, you inspired this obsession with ‘matrescence.’ You saw in my ideas something important, and because of that, I could see it too. Dr. Dinelia Rosa, thank you for trusting me with my very first patient. Dr. Barry Farber, Dr. Ronit Kishon, Dr. Victoria Wilkins, and Dr. Jerome Kogan—thank you all for your formative clinical supervision. - - xv Finally, this piece of work is dedicated to my dear family—it is your work, too. To my wonderful Dad: you gifted me unconditional love, personal freedom and choice, limitless education and purpose by which to live my life. You lead by example in using your voice and your position to ‘speak truth to power,’ as you say. “If I am not for myself, who will be for me? And being for myself, what am ‘I’? And if not now, when?”—I dedicate the mission of this work, to you. To my beautiful sister—you are my intellectual partner, my sample size of 1, and the first person to tell me “This work matters.” You are everywhere in this work, weaved throughout its inspiration; you have shown me what an empowered birth can look like, and I attempt to impart that here—I dedicate the findings of this work to you. Finally, to my beloved Adam—you are my greatest advocate and steadfast partner, accompanying me on every minute of this journey. You helped me to navigate data emergencies (both real and imagined), kept me fed and watered, listened to my lectures for the 1000th time as if it was the first time you had heard that old song and dance, and you reassured me through my doubts. Therefore, it goes without saying—I dedicate the successful completion of this work to you. - - xvi - - xvii “Remember this, for it is as true as true gets: Your body is not a lemon. You are not a machine. The Creator is not a careless mechanic.” -Ina May Gaskin, “Guide to Childbirth” - - 1 CHAPTER I: INTRODUCTION The present study explores the risk factors, correlates, health and mental health outcomes of ‘mistreatment’ in maternity care and childbirth in New York City hospitals (Bohren et al., 2015). Approximately 17% of women experience some form of mistreatment in labor in the United States; this may include lack of informed consent, normalization of unwanted procedures, disrespect, verbal abuse such as coercion or threats, and discrimination (Bowser & Hill, 2010; Vedam et al., 2019; Diaz-Tello, 2016). Consequences of poor childbirth experience may include physical trauma and mental health sequelae such as fear, depression, psychosis, and posttraumatic stress disorder (Antoniou et al., 2021; Sega et al., 2021; Small et al., 2020; Vedam et al., 2019). In New York City, approximately 3,000 mothers die or experience a life-threatening event in relation to childbirth every year (NYC Health, 2018). More women experience severe maternal morbidity in New York City than the rest of the state (3% rate), with rates highest among women of color (NYS Health Foundation, 2020). High maternal morbidity is associated with higher rates of poor childbirth experience and postpartum mental illness (de Graaff et al., 2017). There is currently no routine measure of the quality of maternal and obstetric care in New York City from the perspective of the mothers. The current study aims to address this maternal health gap. The study applied a mixed-methods, cross-sectional approach to examine the prevalence, risk and protective factors, potential “drivers” and mental health consequences of mistreatment in maternity care among a diverse cohort of women who gave birth in New York City hospitals within the past year. To collect the quantitative data, participants completed an online survey comprising yes-or-no questions to assess childbirth information, health history, mental health history, and - - 2 sociodemographic information. Scales examining respect and autonomy during maternity care and labor, as well as scales examining perinatal mental health were also administered. Using these data, the study examined four models of mistreatment in labor using multiple regression and structural equation modeling. To collect the qualitative data, the study conducted semi-structured interviews. These data were transcribed, thematically coded then analyzed utilizing reflexive thematic (RT) analysis. Together, the mixed-methods data were triangulated into an exploratory model of mistreatment in childbirth, towards the respectful and dignified care of birthing persons in New York City hospitals. The findings of this study help to identify leverage points for addressing New York City’s high maternal mortality and severe maternal morbidity rate and to provide translational research on the role of birth experience on perinatal mental health, towards the collaborative care of women in New York City (NYC Health, 2021). Just as “there is no health without mental health,” there is no maternal-child health without maternal mental health (Chisholm, 1954). Multi-determined health phenomena require multi-disciplinary and indeed interdisciplinary research. Thus, the present researcher humbly submits these contributions to slowly “fill” the interdisciplinary maternal mental health research, intervention and treatment “gap” (Kohn et al., 2004). - - 3 CHAPTER II: BACKGROUND Becoming a Mother, Birthing a Mother Becoming a mother is arguably one of the greatest periods of psychological, emotional, and physical upheaval in a woman’s life. One that is marked by new life transitions, complex changes in inter and intrapersonal dynamics, an increased need for social support, and psychological vulnerabilities (Winson, 2009). The process of becoming a mother initiates a monumental shift in human lifespan development. It intersects the spiritual, psychological, medical, financial, cultural, interpersonal and intrapersonal realms of a woman’s life rendering it a necessarily interdisciplinary area of study. Indeed—there does not exist one domain of society that does not in some way inform or impact the experience of becoming a mother or ‘matrescence,’ and that is likewise not consequently impacted by the birth of a mother (Stern & Stern, 1998; Raphael, 2011; Athan, 2020; Chodorow, 1978). The incredibly complex, difficult, and ancient task of mothering is matched only by the process that ushers it in. Pregnancy and childbearing are the most resource-intensive physical feats of which humans are capable; the endurance and metabolic expenditure of a pregnant person exceeds that of the most extreme human athletic feats (Thurber et al., 2019; Trevathan, 2017). Evolution mandates that pregnant people necessarily live on the limits of human exertion, both physically and mentally (Rosenberg & Trevathan, 2002; Raphael-Leff, 1982; Rosenberg, 1992). It follows, therefore, that becoming a mother has historically been marked by hardship, extreme loss, and high mortality throughout our evolution. In response to this veritable teeter-totter of human existence, cultures have developed explanatory models, religious mandates, and supportive traditions around pregnancy, birth and the - - 4 postpartum period (Callister et al., 1996; Miller & Shriver, 2012; Withers et al., 2018). Medical advancements in high-resource nations strive to meet the demands of this dangerous feat while midwives, birth attendants and doulas work tirelessly to usher women through it around the world, every day. Women’s own physiology and psychology have evolved a spectrum of responses to mark, prepare for, and initiate this passage. These include supportive changes such as increased grey matter gains in the brain and positive perceptions of resilience in one’s self following birth, as well as more arduous changes such as feelings of loss, anxiety over one’s capacities to keep an infant alive, and extreme trauma responses (Luders et al., 2020; Meltzer-Brody et al., 2018; Athan, 2011). Childbirth is a formidable experience for women; how she is treated by her treatment team, whether she feels supported by a partner, whether she feels scared or fears for her life, the reality that things don’t go as “planned,” as well as the quality of perinatal medical care she receives together inform how women perceive their childbirth experience. And, how a woman perceives her childbirth experiences directly impacts how she perceives herself as a mother and is of consequence for the development of her maternal self-efficacy (Reisz, Jacobvitz & George, 2015). As such, the utmost care must be afforded to this powerful and formative transition—the stakes are too great. For decades, the study of childbirth has been siloed within public health, midwifery, nursing, psychological and medical camps. This has consequentially led to a false mutual exclusivity between empirical findings from each of these fields; just as the act of becoming a mother is richly symbiotic with anthropology, so too are the phenomena studied under psychology, gynecology, social justice, and others. Increasing evidence demonstrates that biology, psychology, genetics and social determinants of health are interdependent and complexly contribute to - - 5 women’s health outcomes. Concurrently, in recent years the field has witnessed a trend towards integrative and collaborative maternal health care in major U.S. cities. It therefore follows that the empirical study of maternal psychology should necessarily follow suit in a collaborative and integrative empirical investigation by integrating these formerly siloed camps into a single line of research (Lefman, Combs-Orme & Orme, 2017; Kwee & McBride, 2016). As such, the present author issues a ‘call to action’ to maternal psychologists to apply their study of becoming a mother to the work of public health researchers examining the medical sequelae of birth, in order to examine how the interplay of social, cultural, medical and psychological factors may together be contributing to negative experiences of childbirth, and thus, poor maternal psychological adjustment. Every woman deserves choice, knowledge, authority, and gentleness during her birthing process. At the very least, no woman deserves to be traumatized by the systems in place to support her (Chadwick, 2018). As such, the present study aims to “deconstruct” the psychological study of birth by using an interdisciplinary lens to examine the lived experiences mistreatment in maternity care and childbirth in the United States, towards the endless global effort to establish human rights, equity, women’s rights, and dignity in matrescence (United Nations, 2018; Burman, 2008; Athan & Reel, 2015; Chadwick, 2018). Childbirth Experience Factors that Impact Childbirth Experience Globally, a woman gives birth every four seconds. For each birth, there are myriad factors that contribute to a birth ‘experience’ including speed, expectations, birthing position, healthcare context, and supports. “Normal” or “uncomplicated” labor and childbirth can occur very quickly - - 6 (4 hours) or can occur over several days (World Health Organization, 2018) . Normal childbirth can occur standing up, lying down, with support, submerged in water, on a ball or birth chair (Dundes, 1987). Normal childbirth flows at different paces however due to the modern phenomenon of facilities-based birth and the technocratic model of birth, “normal” childbirth expectations or experience may be too narrowly defined, leading to perceived complications, medical interventions, unmet expectations, and a lack of flexibility which impairs individualization and ‘person-centered care’ (Liese et al., 2021; WHO, 2016; Jansen et al., 2013). Birth is not a pathology. However, it is often treated as one within many American medical contexts. Birth is a normal biological process that often elicits fear, anxiety and anticipation in both the birther and her treatment team. As such, the anxiety surrounding birth and the distress tolerance mandated by such an unpredictable process often leads to attempts to over-control and predict an otherwise normal, organic process, which can lead to the perception that birth is dangerous and lead to differences of opinion between the birther and her care-team, especially in the United States (Wolf & Charles, 2018). Birth is a formidable personal experience; however, the experience of birth is highly social in nature. It is impacted by the experience of those caring for the woman and the rights and access afforded to her in her birth. Humans are one of the only species who seek out social support when they labor and birth (Rosenberg, 1992). This support can make or break the birth experience. As such, the type of support a woman receives in childbirth can engender both positive and negative physical and psychological outcomes, explored throughout the present proposal. Mistreatment in Labor How a woman is treated before, during and after birth impacts how she experiences her birth, has consequences for her postpartum psychological adjustment, and informs how she views - - 7 herself (Vedam et al., 2019; Reisz, Jacobvitz & George, 2015). While birth presents an incredible opportunity for power, resilience, and ‘flourishing,’ poor interpersonal and medical treatment in labor can cause immense damage to a woman’s health and mental health trajectory and lead to feelings of powerlessness and dehumanization (Athan, 2011; Vedam et al., 2019; Reisz, Jacobvitz & George, 2015). How providers care for women in birth, how they interact with them, the level of respect they treat mothers with, and their physical interactions with the birthing woman are all correlated with experiences of trauma or mistreatment in birth (Vedam et al., 2019; Hollander et al., 2017; Greenfield et al., 2019). Examples of Mistreatment Mistreatment in labor may be defined as a lack of human dignity afforded to birthing persons and may look different in different contexts. It is estimated that approximately 17% of women experience some form of mistreatment in labor in the United States, although it is difficult to measure, a barrier that the present study aimed to address (Vedam et al., 2019). Examples of mistreatment are common within the maternal health qualitative literature. The voices of women from around the world who have experienced mistreatment help to illustrate the scope of this phenomenon. Some domains of mistreatment include: verbal abuse in the form of scolding, threats or coercive language (i.e. “I was told I was hurting my children and being selfish because I wanted to have a vaginal delivery” and “They said, ‘You can either leave against AMA [Against Medical Advice],’ or whatever, ‘or you can get a C-section,’ and so that was really traumatic for me” and “The provider shouted at me… get up … with no support and while in pain”); physical abuse (i.e. “They were pinching and slapping a client to open up her legs”); sexual abuse (i.e. “I’ve seen ... patients getting exams, and they’re screaming … because it’s so uncomfortable. like you don’t think any better cause … we need to check their cervix ... they - - 8 tell us, “Stop. Stop. Stop. It hurts. It hurts.” And the provider keeps going”); a lack of full and informed consent (i.e. [Pt told the doctor she does not want an episiotomy] “What do you mean?’ That’s my decision. I am the expert here”); ignoring pleas for help or taking too long to respond to pleas for help (i.e. “I told my care provider I have the urge to push down and requested for help …he said I just examined you (you are not yet ready) and ignored me and continued playing with his mobile phone …”); inappropriate medical care (“I was asked to change beds without being properly cleaned and my blood still dripping on the floor…”); and, a lack of respect for the patient’s decisions and autonomy over her own care (“My OB said that I risked having child protective services being called if I refused antibiotics due to being GBS positive” and “When I refused to be induced-even after I was a couple days “overdue” I seriously started to feel like *I* was the problem. It was horrible.”) (Sega et al., 2021; Bohren et al., 2015; Gebremichael, 2018; Salter et al., 2023; Burrowes et al., 2017; Wolf & Charles, 2018; Vedam et al., 2019). These experiences are correlated with feelings of dehumanization, betrayal, trauma, and sadness. In what could potentially be the most powerful process in the course of human development, women’s lives have been forever changed by mistreatment in labor. These forms of mistreatment have been identified by different names depending on the literature, however some common typologies include: traumatic birth, obstetric violence, obstetric iatrogenesis, disrespect & abuse in labor, and, mistreatment (Bowser & Hill, 2010). Obstetric Violence Obstetric violence has been identified in the literature as the delivery of unnecessary interventions, the provision of non-evidence-based care, and threats of violence and coercion from providers (Diaz-Tello, 2016; Bowser & Hill, 2010). It is a form of domination by people in positions of power to exercise control over a woman in a vulnerable state (Brief for National - - 9 Advocates for Pregnant Women as Amicus Curiae, 2016) Following experiences of obstetric violence, women report feelings of betrayal, threats to their sense of security, and feelings that their autonomy has been violated (Diaz-Tello, 2016). In sum, obstetric violence is violence against women perpetrated by the very systems which aim to protect, foster and heal them (Sadler et al., 2016). The experience of obstetric violence may lead to “traumatic births” or births marked by the experience of fear, trauma, and a perception of danger. Disrespect & Abuse The term disrespect and abuse in labor (D&A) is one typology historically used to describe the phenomenon of mistreatment, especially in the public health literature. It has been defined as: “physical abuse, non-consented care, non-confidential care, non-dignified care (including verbal abuse), discrimination based on specific attributes, abandonment or denial of care, and detention in facilities” during childbirth (Bowser & Hill, 2010). Studies have found that 1 in 6 American women experience this form of mistreatment during labor (Vedam et al., 2019). D&A in labor is not well-examined in the psychological literature in the United States, with the majority of research focusing on low-resourced nations with less developed health infrastructures. In recent years, D&A in the United States has gained more attention in the nursing, midwifery and obstetrics and gynecology literature (Bowser at Hill, 2010). In response to legal action in 2011 and 2013 addressing D&A, interest has grown in improving the birth experience of American women with social media movements such as #BreakTheSilence which helps women share stories of traumatic or disrespectful birth (Morton et al., 2018). In lower-income and low-resourced regions, D&A in labor may take the form of limited resources, physical abuse or birthing alone. In high-income and high-resourced regions, such as the United States, disrespectful maternity care may take on more implicit or systematic forms of - - 10 abuse. These include non-consent for pelvic exams, non-consent for episiotomy, as well as: “fear tactics and lack of balanced information to push a woman to have a cesarean section, dismissing birth plans as ‘ridiculous,’ and limiting freedom of movement” (USAID, 2012). Further research collected from first-hand accounts of labor and delivery nurses in Canada and the United States have found that nurses “frequently witnessed verbal abuse in the form of threats to the baby’s life unless the woman agreed to a procedure, and failure to provide informed consent” (Horton et al., 2018). ‘Obstetric Iatrogenesis’ More recently, the field has employed a term that encompasses both obstetric violence and D&A in labor. In the 1970’s, the term ‘medical iatrogenesis’ emerged to identify, “ injuries done to patients by ineffective, unsafe, and erroneous treatments” (Illich, 1976; Liese et al., 2021). Contemporary researchers have adapted this to coin the term ‘obstetric iatrogenesis’ which includes unintentional harm, disrespect, overt violence and abuse (Liese et al., 2021). Together, the term “unintentional harm, disrespect, violence, and abuse” (UHDVA) has been introduced to encompass phenomena inclusive of obstetric iatrogenesis and D&A. Normalized and systematic forms of mistreatment may be captured under “unintentional harm,” whereby providers may not recognize their treatments as abusive or coercive. The authors explain that this term ‘unintentional harm’ may be a function of the “obstetric paradox” of American childbirth whereby procedures intended to make birth “safer” in fact cause more harm (Liese et al., 2021). An example of this may be the artificial rupture of membranes, Pitocin inducement of labor, episiotomy surgery, or universal protocols for when to conduct Cesarean section. These procedures are often widely accepted and used in medical practice, and therefore may not be recognized as harmful or women may not feel able to refuse the intervention, thus - - 11 leading to perceptions of mistreatment and overall negative birth experiences that may be perceived as acceptable. Mistreatment: Defining a Phenomenon While the aforementioned typologies of mistreatment evidently share common features, the lack of cross-field consensus in typology, definition and measurement has rendered the systematic study of mistreatment challenging. In an effort to address this concern and to explore the public health implications of mistreatment in childbirth, the World Health Organization commissioned a global review of mistreatment, abuse, disrespect, loss of autonomy and violations of human rights in labor (Vedam et al., 2019; Bohren et al., 2015; World Health Organization, 2015). In 2015, Bohren and colleagues, released their report describing seven dimensions of mistreatment in maternity care that have negative impacts on the health outcomes of women. At the time of this study, there did not exist a uniform approach to the definition and measurement of the mistreatment of women during childbirth, as such, the researchers conducted a mixed-methods review of available research to develop a typology of mistreatment in childbirth facilities globally. The Seven Dimensions of Mistreatment The seven dimensions of mistreatment identified by Bohren and colleagues encompass the categories included in obstetric violence, disrespect and abuse and other typologies of reference to mistreatment. These include: (1) physical abuse, (2) sexual abuse, (3) verbal abuse, (4) stigma and discrimination, (5) failure to meet professional standards of care, (6) poor rapport between women and providers, and (7) health system conditions and constraints (Bohren et al., 2015). The report summarily outlined these phenomena in both high and low resourced regions and issued a call to action to begin the systematic study, measurement and exploration of these phenomena, towards reducing human rights violations and towards equity in healthcare, and the protection of women’s - - 12 rights. Further, it encouraged future researchers to utilize uniform typology of ‘mistreatment’ to describe these dimensions, rather than other common terms such as: D&A, “Respectful Maternity Care” (RMC), “maternal distress,” and “unintentional harm, disrespect, violence, and abuse” (UHDVA) (Vedam et al., 2019; Bohren et al., 2015; World Health Organization, 2015; Emmanuel & St. John, 2010; Liese et al., 2019). Typology: Focus on Mistreatment Evidently, typology and terminology are in flux regarding the study of mistreatment in labor, depending upon one’s field of study. Critical feminist theorists and some global health researchers prefer the language of D&A as it is seen to represent the phenomena in equitable terms and is illustrative of the basic human rights component of the phenomenon. However, the World Health Organization prefers the term “respectful maternity care” (RMC), as this language frames the problem as an issue of healthcare quality while also attending to the human rights piece. Further, the neutrality of the term theoretically allows for the buy-in that is needed from the providers themselves (Sen, Reddy & Iyer, 2018). The most recent studies have utilized “unintentional harm, disrespect, violence, and abuse” (UHDVA) to capture both the passive and active forms of mistreatment, within the context of obstetric iatrogenesis (Liese et al., 2021). The current study, however, will follow the recommendations of Bohren and colleagues, and utilize “mistreatment” as it captures both the experience of medical, psychological, and civil experiences in labor (Morton et al., 2018). In doing so, the present study follows suit on the most recent mistreatment studies in America to continue to build upon the work of Vedam and colleagues, Salter and colleagues, and Morton and colleagues (2019; 2023; 2018). - - 13 Mistreatment by Region Lower-Resource Regions To date, much research on maternal treatment and disrespect in birth has focused on lower-resourced regions where mistreatment may more often take the form of physical abuse by providers, being ignored in labor, or cries for help going unanswered (Bowser & Hill, 2010). In lower-resourced regions, this is often accounted for by provider burnout, large medical care treatment gaps, and low access to appropriate medical care. However, women in high resource regions also report mistreatment; it is often in the form of lack of full and informed consent, unwanted procedures, verbal abuse, threats, and maternal infant separation without explanation (Bowser & Hill, 2010; Vedas et al., 2019; Bohren et al., 2015). High-Resourced Regions Recent studies have begun to examine the phenomenon of mistreatment in high-resourced regions, including the United States. Here, mistreatment may take the form of lack of informed consent, normalization of unwanted procedures, and verbal abuse such as coercion or threats (Bowser & Hill, 2010; Vedam et al., 2019). Studies have endeavored to capture the scope and prevalence of this phenomenon in the United States. Morton and colleagues conducted a large-scale study of maternity care quality in North America from the perspective of labor and delivery (L&D) nurses. Over half of L&D nurses reported witnessing providers conducting procedures without providing alternate options or time to consider them. Further, 20% of L&D nurses witnessed providers engaging in procedures explicitly against the patient’s wishes (Morton et al., 2018). This study helps to estimate the frequency of implicit and overt mistreatment in American labor, and points to the necessity for increased study in this area. - - 14 A more recent study in Pennsylvania, United States interviewed 28 doctors, nurses, midwives and other healthcare providers and explored the frequency and forms of Bohren’s seven dimensions of mistreatment (Salter et al., 2023). The study found that providers had witnessed all seven categories of mistreatment including sexual abuse, verbal abuse and a lack of informed consent in American medical practice. With respect to research conducted from the perspective of the mothers themselves, the most recent studies of maternal mistreatment in perinatal care and childbirth in the United States found that 17% of women experience at least some form of mistreatment (Vedam et al., 2019). A significant number of respondents reported “being ignored” or that “providers failed to respond to their requests for help” or were subject to unnecessary or wanted invasive procedures (Vedam et al., 2019; Salter et al., 2023; MMHLA, 2023). However, there remains a dearth of studies pursuing these important experiences here in the United States. Possible Impacts of Mistreatment in Labor Mistreatment in labor, traumatic birth or disrespectful birth can have consequences for mental health, mother-infant interaction, trust, and family relationships. It can also negatively impact future patient engagement with the healthcare system (Vedam et al., 2019). In the short-term, the impacts of mistreatment in labor are suffering, pain, and feelings of loss and discouragement. The long-term impacts may include trauma symptoms, tokophobia (or fear of childbirth), negative views of one’s body, and feelings of dehumanization (Sega et al., 2021; Wiljma, Wiljma, & Zar, 1998; Vedam et al., 2019). Other consequences of poor childbirth experience may include physical trauma, severe maternal morbidity, physical scarring, and mental health sequelae such as fear, depression, psychosis, and posttraumatic stress disorder (Antoniou et al., 2021; Sega et al., 2021; Small et al., 2020; Vedam et al., 2019; Salter et al., 2023). Most - - 15 poignantly, the mistreatment itself is a negative outcome as it is a fundamental violation of human rights (Morton et al., 2018; Vedam et al., 2019). Mental Health Implications Separately from the maternal mistreatment literature, there exist robust data in the medical and psychiatric literature on the correlation between severe maternal morbidity (SMM) and subsequent psychiatric illness (Lewkowitz et al., 2019; Small et al., 2020). The experience of near-death, horror, fear, loss of autonomy, and violation of one’s bodily control together pose risk factors for psychological distress, potentially leading to perinatal mental health complications, such as post-traumatic stress disorder (PTSD) and major depression (Sega et al., 2021). This loss of autonomy and feelings of “powerlessness” that occur in negative or traumatic birthing experiences have been correlated with increased risk for PTSD, postpartum depression (PPD), and fear of childbirth (tokophobia) (Lukasse, Schroll, & Karro, 2015; Vedam et al., 2017; Beck, 2004; Alcorn et al., 2010). Posttraumatic Stress Disorder About 45% of women perceive birth to be “traumatic,” which may occur when a woman perceives that she or her baby may die, endures severe morbidity, or endures other near-death experiences in labor (de Graaff et al., 2017; Yildiz et al., 2017). Violations of human rights in labor (i.e., lack of consent, verbal and physical abuse, loss of control over decision-making) can lead to feelings of helplessness, “powerlessness,” and life-threatening fear, which may lead women to develop acute stress responses including PTSD (Beck, 2004; Manning et al., 2020; Alcorn et al., 2010). Acute stress responses in labor comprise a form of severe maternal morbidity (SMM) (Salter et la., 2023). As a result of these mistreatment experiences in labor CB-PTSD, or PTSD - - 16 which occurs in the context of a traumatic childbirth as the triggering event, may be diagnosed in the context of PTSD symptoms (Kranenburg, Lambregtse-van den Berg, & Stramrood, 2023). Traumatic birth has been shown to re-trigger traumatic memories of sexual assault or sexual violence, which can initiate a recurrence of previous trauma symptoms and can engender re-traumatization (Beck, 2004; Manning et al., 2020; Wosu et al., 2015). Studies show that between 4% and 18% of American women who give birth go on to develop PTSD, with rates being higher in groups who are ‘high-risk’ for medical or mental health complications (Yildiz et al., 2017; de Graaff et al., 2017). Depression & Anxiety Of further import is the correlation between maternal mistreatment in labor and postpartum depression (PPD). PPD is an impairing illness, impacting about 11% of American women and up to 20% of New York mothers (CDC, 2020). PPD is one of the most debilitating conditions experienced by women in their childbearing years, however it is a fairly common condition: PPD constitutes a leading cause of disease burden for women of childbearing age (O’Hara, 2009; WHO, 2011; Vos et al., 2016). Globally, the pooled prevalence for PPD is 12% however this varies by country, income-level, social capital, and women’s place in society, along with other country-level variables and social-determinants of health (Woody, 2017; WHO, 2014). PPD symptoms comprise symptoms typical of major depression including neuro-vegetative symptoms such as impaired sleep, appetite disturbances, psycho-motor changes and loss of energy, as well as anhedonia, guilt, sadness, increased crying and worry (APA, 2014). In PPD, the depression may orient towards negative views of one’s self-efficacy as a mother, self-blame or shame for feeling sad regarding one’s infant, and worry for one’s own safety or anxiety for the safety of the baby. Feelings of isolation and anxiety may be more prominent in a PDD - - 17 presentation, as compared to other depressions (Field, 2010; O’Hara, 2009; APA, 2014). Because PPD can lead to serious mental health and health impairment in women, it can also have serious consequences for the social, emotional and economic health of the family and can place a strain on the healthcare system overall. Studies have found that the experience of mistreatment in childbirth, in the form of verbal or physical abuse, increases the odds of developing PPD symptoms (Silviera et al., 2019). Further, studies have linked the experience of the trauma of an unplanned C-section to increased PPD symptoms, citing negative medical interactions as an important factor in the perceived birth trauma (Sega et al., 2021). Given the debilitating nature of PPD and its myriad negative impacts, it is important to understand the relationship between the experience of mistreatment and later PPD, and how quality of maternity care may moderate the well-established relationship between lifetime history of depression and PPD (Guintivano, Manuck & Meltzer-Brody, 2018; Sega et al., 2021). With respect to mood disorders, for women with a psychiatric history of bipolar disorder, the experience of a traumatic birth may put her at a higher risk for postpartum psychosis, a highly uncommon but staggeringly debilitating disorder that necessitates acute hospitalization and psychiatric treatment (Antoniou et al., 2021). Suicide Some mothers suffering with PPD go on to develop suicidal symptoms. Sadly, studies suggest that suicides may account for up to 20% of postpartum deaths globally (Campbell et al., 2021). In the United States, pregnancy-related suicide is a top five cause of maternal mortality and has increased significantly in the last decade (Campbell et al., 2021; Admon et al., 2021; Goldman-Mellor, & Margerison 2019). Recent research has demonstrated that maternal suicide and self-injury is higher than previously reported. Limitations in reporting suicide in American death - - 18 statistics and the cultural bias against the idea of a suicidal mother have together rendered this a difficult area of study (Mangla et al., 2019). Research has demonstrated a correlation between maternal opiate and drug use and the risk for perinatal self-injury, suicide and overdose death, which is of import to the study of mistreatment in labor (Campbell et al., 2021; Mangla et al., 2019; Goldman-Mellor, & Margerison, 2019). A recent finding demonstrated that women who have current or past substance use history are more likely to experience mistreatment in labor than those who do not, thus compounding risk for postpartum mental illness (Vedam et al., 2019). As such, the current researcher posits that perinatal suicidality may be an important and preventable distal consequence of mistreatment in American labor. Therefore, evaluating the mental health sequalae of mistreatment may present an important opportunity to prevent maternal death by suicide. Mother-Child Relationship Postpartum bonding How women are treated in birth and how they experience their birth can impact postpartum adjustment, how mothers view themselves, and can inform the quality of attachment between mother and baby. Studies have found that women who experience traumatic birth have difficulty bonding with their infant, have lower self-efficacy, and decreased confidence in their ability to parent (Mayopoulos et al., 2021; Reisz, Jacobvitz & George, 2015). Therefore, the present study explored the relationship between mistreatment and postpartum bonding, and the way in which mistreatment may act to moderate the role of prenatal risk factors, such as depression, on postpartum bonding ability. - - 19 Giving Birth in America: Mortality and Morbidity Maternal Mortality and Severe Maternal Morbidity The United States has the highest perinatal maternal mortality rate (MMR) of comparable peer, high-resourced and high-income nations and this rate is continuing to rise (World Health Organization, 2019; NYS Health Foundation, 2020). According to the Centers for Disease Control and Prevention, 1,205 women died of maternal causes in the United States in 2021. This means the average MMR for 2021 was 32.9 maternal deaths per 100,000 live births, up from 23.8 in 2020 and 20.1 in 2019 (Hoyert, 2023). Rates among women of color are even higher; the MMR for Black birthing people in America is 69.9 maternal deaths per 100,000 live births, more than double the MMR for Caucasian women (Hoyert, 2023). This appalling maternal mortality rate is simply the “tip of the iceberg” of poor maternal health outcomes. Of the women who survive childbearing in America, many still endure severe maternal morbidity (SMM), or injury caused during or after labor (NYS Health, 2020; CDC, 2020). The high rate of SMM in the United States (about 50,000-60,000 women/year) corresponds to the experience of ‘traumatic birth,’ mistreatment in labor and poor treatment in labor, which have been identified as consequences of SMM (CDC, 2020; Fink et al., 2023). The rate of SMM is also rising in the United States (Fink et al., 2023). Reducing Mistreatment as an Avenue for Reducing Mortality This trend of a loss of autonomy in labor and disrespectful maternity care is unacceptable: not only because this is within the context of a high resource setting where the usual drivers of mistreatment (such as poor provider to patient ratios) are not present, but also due to the high maternal mortality rate in the United States. Recent data on maternal mortality links providers’ delayed response or lack of timely attention to women’s requests for care (both forms of - - 20 mistreatment) to the clinical signs to maternal mortality (Vedam et al., 2019). This suggests that mistreatment may be a driver of the high maternal mortality in America (California Maternal Quality Care Collaborative, 2018; Vedam et al., 2019). As such, attending to the phenomenon of mistreatment in labor presents a powerful research and public health opportunity to understand the non-medical drivers and potential social determinants of maternal mortality in the United States, such as mistreatment in labor, and to help develop a literature around the psychological sequelae of the phenomenon (WHO, 2014). Focus on: New York City In New York City, the most recent measure of MMR found that there are 36.7 maternal deaths per 100,000 live births (NYC Health Department, 2022). This is significantly higher than the New York State MMR of 21.7 per 100,000 (CDC, 2023). 3,000 mothers die or experience a life-threatening event in relation to childbirth every year in New York City (NYC Health, 2018). More women experience severe maternal morbidity in New York City than the rest of New York state (3% rate), which is already one of the American states with the highest MMR (NYS Health Foundation, 2020). In response to increased attention on traumatic birth and obstetric violence in recent years, New York City created the Merck for Mothers Grant in 2018, aimed at helping hospitals to monitor severe maternal morbidity through a Maternal Hospital Quality Improvement Network. This network of 23 hospitals set out to train hospital staff to identify and prevent mistreatment in labor (NYC Health, 2018). While educating and assessing providers on the topic is important, there remains little to no available data on the women’s experiences of this mistreatment and SMM in New York City. Without such data, there is no way to meaningfully compare hospitals by mistreatment levels or to initiate policy reform towards improvement, or to hear from the women themselves what they - - 21 feel impacted their experiences of mistreatment in labor. To the author’s knowledge, there is no routine, valid, or reliable measure of quality of maternal and obstetric care from the perspective of the mothers in New York City hospitals, despite the Merck for Mothers initiative. The current study aims to address that maternal health gap. With respect to regional differences, few studies on mistreatment in labor have focused on a specific city in the United States. Given the outstanding economic, racial and education diversity in New York City, as well as the higher-than average severe maternal mortality rate, it would be of great clinical and civic value to begin this study of mistreatment in the largest city in America. Preliminary data collected in New York City suggest that region and place of birth may help to explain some of the disparities in birth outcomes, MMR and SMM. Women who live in the Bronx or in Brooklyn are 2-3x more likely to die due to pregnancy-related complications, than a woman who lives in Manhattan (19% Manhattan, 28% Bronx, 33% Brooklyn of all pregnancy-related deaths) (NYC Health, 2021). Regional differences in care, funding and hospital access may help to explain some of the MMR outcomes by race, but studies between women of different rates in the same borough of New York City may shed light on the role of mistreatment as informing these data, when controlling for other sociodemographic variables. The present study proposes to be one of the first mixed-methods studies on mistreatment to focus on a specific region within the United States. Risk Factors for Mistreatment in American Childbirth Race Of import is the great disparity in maternal health outcomes by race in America. Studies have demonstrated that women of color have a 12x higher rate of maternal mortality than white women in New York (NYC Health, April 2021). While it is true that correlates of race such as - - 22 income-level, insurance-status, housing status and interaction with the healthcare system in the prenatal period may all contribute to health outcomes, studies have demonstrated that when these are controlled for, there remain medically unexplained discrepancies in maternal morbidity and mortality that may be accounted for by maternal mistreatment due to racism and discrimination (Vedam et al., 2019). Studies on maternal mistreatment present an important avenue for unpacking this worrying trend in maternal mortality. Women of color, including Black, Hispanic and Indigenous women are nearly three times as likely to experience mistreatment in labor in the form of being coerced into an intervention they do not want, experiencing verbal abuse, or not being attended to in a timely manner (Vedam et al., 2019). These forms of mistreatment put women of color at increased risk for severe morbidity and mortality due to medical neglect, hemorrhage, and untimely medical responses (Wynn, 2019). As such, the experience of mistreatment may compound these risk factors, and thus be contributing to the high SMM and MMR among mothers of color. Race and “High-Risk” Pregnancy Further, researchers have also demonstrated presence of systemic racism as a mediator in the relationship between race and maternal health outcomes. For example, infant health outcomes are impacted by race: women of color are more likely to have infants who are low birth weight (LBW) (Rosenberg et al., 2005). Many medical journals point to “high-risk” pregnancy variables such as higher rates of BMI>40, hypertension, and gestational diabetes as accounting for this, and indeed there exist higher rates of these aforementioned complications among Black women in the United States as compared to their Caucasian counterparts (Rosenberg et al., 2005). However, research on the effects of systemic racism in healthcare have established a correlation between maternal lifetime experiences of racism and LBW infants, with women of color being subjected to - - 23 life-long higher rates of chronic stress which can increase allostatic load and impact overall maternal health and subsequent fetal development (Riggan, Gilbert & Allyse, 2021). The link between maternal medical risk factors such as gestational diabetes, and birth outcomes such as LBW is not a simple one. Studies have identified that this relationship may be partially determined by experiences of racism, discrimination or maltreatment in perinatal care, due to these high risk designations. Therefore, further study of the role of mistreatment in moderating the relationship between these risk factors is warranted (Vedam et al., 2019). Social and Economic Risk Factors Along with a woman’s race and ethnicity, insurance status, housing status, immigration status, income-level and region of birth all present potential social determinants of maternal health (Morton et al., 2018; WHO, 2014). Public health researchers have identified these as common “drivers” of disrespect and abuse in perinatal care (Sen, Reddy & Iyer, 2018). Further, studies have found that women who are younger mothers (<24 y/o), those who have been incarcerated, have substance use histories, have a Black partner in the birthing room, and who are housing unstable are statistically significantly more likely to report at least one form of mistreatment, with odds increasing when intersectionality is examined (Morton et al., 2018; Vedam et al., 2019; Salter et al., 2023). Parity. A further finding is that parity may be a significant predictor of mistreatment, with multiparous women (those who have previously given birth) reporting less mistreatment, while primiparous (first time giving birth) mothers report higher rates of mistreatment. This is possibly due to the impact of having gone through the experience before and multiparous women knowing - - 24 what to expect, or, possibly due to a normalization of an otherwise traumatic experience that makes women less likely to report mistreatment in their second birth (Vedam et al., 2019). Access Unfortunately, choice in how, when and where one gives birth is not an option for most American women. 98.4% of women give birth in hospitals in America, with less than 2% choosing to give birth in a planned home-birth or birthing center (National Academies of Sciences, Engineering and Medicine, 2020). Limited to midwifery services and birthing centers, insurance coverage, knowledge of options, the high-cost of birth and American medical culture all influence this phenomenon. Most American women give birth in their local hospital where quality of care, presence of specialists such as lactation consultants or reproductive psychiatrists, neonatal treatment capacity and education of staff may vary widely by region, funding, availability of physicians, and population density (Kozhimannil et al., 2016). For some women in America, their closest hospital is hours away. As such, there is a paucity of options and lack of access to care for rural maternal populations (Cromartie et al., 2020). While distance is less of a barrier in New York City due to its high volume of hospitals (some of them the best in the world), financial constraints present a considerable barrier to access for New York City mothers (Johnson et al., 2020). Women who choose where they give birth, tend to have more positive perceptions of their birth experience and less interventions (Coxon et al., 2017). Therefore, it follows that income-level and access are determinants of birth experience. Cost of Giving Birth Even with insurance, the cost of childbirth in New York city is highly prohibitive and presents a huge stressor to birthing families. New York State is the most expensive place on earth to give birth, according to a recent study, with the average birth costing around $20,000 among - - 25 commercially insured persons (Johnson et al., 2020). In Manhattan, the cost may range from $2,000 to $42,000 depending on hospital costs, mode of delivery (C-sections can cost twice as much as vaginal delivery), physician fees (which vary by practitioner even within the same hospital) and insurance provider (NYS Health Foundation, 2021). While Medicaid provides a good solution to this dilemma, some of the “best” physicians do not accept Medicaid, thus skewing childbirth outcome data further by determinants of access and health such as income-level (Lewis, May 2021). The high cost associated with midwifery and homebirths is prohibitive for most women, however these birth modes are associated with more positive perceptions of birth and healthier birth outcomes, which may be a function of access, choice, self-selection of individuals who have more childbirth knowledge, and financial stability (Vedam et al., 2019; Coxon et al., 2017; Souter et al., 2019). However, given that most women give birth in hospitals in New York City, and the study’s intent was to explore interactions between insurance status, income and other determinants of home-birth access, hospitals are the focus of the present study. Medical Complications: “High-Risk” Pregnancies Pregnancies that are considered high-risk or complicated are also considered a risk factor for poor birth outcomes. For example, women of an advanced maternal age (formerly known by the dreaded term, “geriatric pregnancy”), women with unmanaged hypertension, gestational diabetes and high body mass index (BMI) in pregnancy are at a higher risk of having low birth weight infants (LBW), preterm deliveries, preeclampsia, and Cesarean section delivery (Rosenberg et al., 2005). This is a complicated statistical relationship given the established correlation between race, poverty, access and these aforementioned risk factors (Rosenberg et al., 2005). For example, it is well established that women of color have disproportionately more LBW - - 26 infants than non-minority women, when holding for poverty, neighborhood and other associated risk factors (Orchard & Price, 2017). This identifies the role of race and systemic racism in maternal and women’s healthcare, reflected in infant health outcomes, as discussed in the previous section. Body-Mass Index (BMI) and Stigma While having a high BMI is associated with poorer birth outcomes, more recent research has examined the role of stigma and internalized stigma among obese pregnant women and the impact of internalized stigma on birth outcomes (DeJoy & Bittner, 2015). In fact, obesity stigma has been linked to negative birth outcomes including macrosomia (larger than average fetal size), preterm birth and a high rate of C-section deliveries (Lim & Mahmood, 2015). The high rate of C-section deliveries in the United States (~32%) is correlated with more unnecessary medical interventions, injury and negative mental health sequelae (Stephenson, 2022). The ideal average C-section rate for humans is 10-15% (WHO, 2015). Any unnecessary surgical intervention is, by definition, something that should be avoided at all costs, rendering the high Cesarean section rate in the United States an ironic outlier among high-income/high-resourced nations (Betran et al., 2016; Miller et al, 2016). In the medical literature, this C-section rate is often explained by the high number of births among mothers with high BMI (>30) (Lim & Mahmood, 2015). However, this explanation fails to take into account the powerful impacts of social determinants on medical and mental health outcomes (WHO, 2014). Interdisciplinary research on the role of “fat shaming” and internalized stigma sheds new light on the correlation between C-section and poor birth outcomes and suggests there is a need to explore how the attitudes and social interactions of providers with overweight pregnant people may moderate the impacts of BMI on birth outcomes (Ward & MacPhail, 2019). Studies show that - - 27 the experience of stigma may lead to depression, dependence on poor coping mechanisms (such as overeating) or avoidance of healthcare usage due to the expectation of poor and stressful interactions with providers, all of which can aggravate the proximal effects of obesity on maternal-fetal health (DeJoy & Bittner, 2015; Puhl & Heuer, 2010). Further, studies have found that discrimination and weight bias in women’s healthcare has been shown to reduce women’s engagement with reproductive health services (Ward & MacPhail, 2019). One study found that women with “elevated pregnancy risk” experienced higher rates of mistreatment in labor. This included women who fell into the following categories: pre-pregnancy BMI of 40 or higher, carrying twins, self-reported high blood pressure, gestational diabetes or other health complications during pregnancy (i.e., breech position, preterm labor) (Vedam et al., 2019). This powerful preliminary data suggests that childbirth experiences of respect, autonomy, decision-making power, and verbal and physical treatment may act to compound, mediate or moderate these already powerful relationships between health and sociodemographic factors and birth outcomes, in a way that has not yet been thoroughly examined. As such, given the high-rate of obesity in the United States f and the pregnancy complications associated with it, it is of public health importance to understand the determinants and mistreatment implications of a “high-risk” pregnancy (DeJoy & Bittner, 2015; CDC, 2022). To date the moderating role of mistreatment between obesity and birth outcomes has not been extensively explored and poses an important avenue to addressing several public health crises in maternal health. Addressing the prevalence and impacts of stigmatizing or discriminatory provider-patient interactions via the study of mistreatment may present an important avenue for reducing C-section rate overall, but has yet to be explored (Bohren et al., 2015; DeJoy & Bittner, 2015; Puhl & Heuer, 2010). - - 28 COVID-19 Finally, preliminary research has shown that birthing during the COVID-19 pandemic presented a risk factor for increased obstetric violence, such as: unnecessary interventions done with neither medical indication nor evidence-base that it would prevent spread of the virus, being unable to bring a partner into the birthing room, and immediate maternal-infant separation (Sadler, Leiva, & Olza, 2020). Preliminary data found that women who gave birth during the COVID-19 pandemic experienced higher rates of traumatic childbirth, including an increased acute stress response to birth as related to fear of virus contraction and limited visitor restrictions during labor; this led to increased childbirth-related PTSD (CB-PTSD) and mother-infant bonding difficulties (Mayopoulos et al., 2021). The pandemic’s impact on the treatment of birthing women, the arbitrary medical interventions endured by this cohort of women, and the negative mental health impacts of birthing during COVID-19 together highlight the importance of examining mistreatment in American labor where extant violations of autonomy and practice of unnecessary interventions were not only revealed but exacerbated by the COVID-19 pandemic (Vazquez-Xu, 2020). The findings of these studies arguably not the result of a one-off, COVID-19 vacuum. To the contrary, COVID-19 presents a case study and helped to put a spotlight on the impact of mistreatment on maternal health and mental health—mistreatment that was present far before COVID-19 highlighted it. Protective Factors for Mistreatment in American Childbirth Childbirth Education Studies have demonstrated the importance of childbirth education in impacting one’s actual and perceived outcomes of childbirth. As with other endeavors, knowledge functions to - - 29 increase choice and empowerment around birth and the postpartum period. Women with increased childbirth and postpartum knowledge peripartum demonstrate better postnatal mental health outcomes and endorse more satisfaction with birth experience, regardless of actual birth outcome (Citak et al., 2020; Koehn, 2002). While childbirth education is a common form of maternal knowledge-sharing in the United States, women also receive knowledge through informal networks: other mothers, their parents, professionals, social media, and religious groups. Childbirth education has been demonstrated to reduce perceived pain among birthing mothers, possibly due to rehearsal of cognitive coping techniques for relaxation skills, however the data are not in agreement as to whether childbirth education impacts the mode of delivery or frequency of interventions (such as episiotomy or C-section) (Citak et al., 2020). Birth Plans One benefit of childbirth classes is the creation of a birth plan whereby women are assisted in thinking through the choices they will need to make in labor; ideally this also provides education on patient rights and helps women to begin to imaginally prepare for the birth process. There exists a dearth of systematic studies on the impacts of birth plans, and existing studies are not in agreement as to the impact of birth plans on perception of birth and actual birth outcomes (Bailey, Crane & Nugent, 2008). Some findings suggest they help to reduce fear and pain, and that having a birth plan increased the perception of choice and control leading to a positive perception of one’s birth, regardless of actual outcome (Lundberg, Berg & Lindmark, 2003; Cook & Loomis, 2012). While others found that it had no impact on their autonomy or decision-making process (Whitberg & Hillman, 1998). Given the association between birth preparation (plans and education) and perceived experience of birth, the role of birth plans in moderating one’s treatment in labor provides an important avenue for moderating mistreatment that requires further exploration. - - 30 Knowledge of Patient Rights As competent, sound-minded adults in a democratic society, birthing persons in the United States are afforded the right to full and informed consent and the right to refusal of treatment (Bower & Hill, 2010; NYC Health Department, 2023). Despite this, extensive quantitative and qualitative research has demonstrated that these fundamental human rights are routinely ignored, violated, or disrespected during birth (Wolf & Charles, 2018; Bowser & Hill, 2010). Many women report they do not know are allowed to say no to procedures, they feel pressured to agree with decisions made by their doctors, and shockingly, many report they have to “fight” for their right to request or deny a particular mode of delivery during the most vulnerable and challenging feat of their lives (Ibrahim et al., 2021; Wolf & Charles, 2018). These experiences allude to provider-patient disagreement, and the way disagreement is handled has implications for mistreatment experience (Bohren et al., 2015). Therefore, it stands to reason that it is important to develop an understanding for the extent to which women understand their rights as patients and know their right to informed consent, even in birth, as this knowledge may impact the treatment they ask for or experience in birth. Drivers of Mistreatment in America American Medical Culture : “Too Much Too Soon” Obstetric Interventions Mistreatment may look different in different countries due to resource-level differences and degree of medical intervention in birth, by country. Studies have found that at either end of the extremes of maternal care, exists maternal mistreatment. Miller and colleagues coined the terms, ‘too much too soon’ and ‘too little too late’ obstetric and maternal care to describe these extremes (Miller et al., 2016). These capture at one end of the spectrum a paucity of appropriate - - 31 and timely medical interventions and at the other, a glut of medical interventions with too high a frequency and too fast (Miller et al., 2016). Per the World Health Organization’s designations, the United States is considered a ‘too much too soon’ intervention medical culture whereby women may be enduring severe morbidity and mortality due to an over-provision of medical care, just as women in lower-resourced regions are enduring morbidity and mortality due to too little. Unnecessary C-Sections For example, America’s Cesarean section rate is up to three times the medically indicated average (WHO, 2015; Stephenson, 2022). Further, the C-section rate varies significantly by hospital, race, region and by doctor when holding for factors like BMI and twins-pregnancies. As such, this variability indicates a certain percentage of “medically unnecessary” interventions (Miller et al., 2016; Vedam et al., 2019; Betran et al., 2016; Stephenson, 2022). While C-sections can be necessary and life-saving procedures, or just the personal choice of a woman (which is her right), the World Health Organization’s Monitoring Emergency Obstetric Care Handbook has determined that the ideal rate of C-sections is between 10 and 15% (Averting Maternal Death and Disability & WHO, 2009; WHO, 2015). Anything less than 5% and it is likely women are dying, anything more than 15% and they are likely medically “unnecessary.” America performs double the maximum acceptable rate of C-sections, per WHO guidelines. This form of ‘too much too soon’ medicine is common in the United States and has been found to be a driver of obstetric iatrogenesis and mistreatment (Liese et al., 2021). However, studies examining the impacts of ‘too much too soon’ interventions on mistreatment have failed to comprehensively examine the mental health outcomes of those affected. - - 32 Global Mental Health Lens: The Paradox of Maternal Mistreatment in America In using a global mental health lens to approach the study of mistreatment in American birth, cultural factors must be understood and explored. One may ask: Why study the phenomena of mistreatment in the United States, when rates are so high elsewhere? While estimates find that less than 20% of women experience mistreatment in America (it may be as high as 60% in lower resourced regions) this rate is nonetheless unacceptably high especially given the high-resourced context of American childbirth (Vedam et al., 2019; Bohren et al., 2019; MMHLA, 2023). These mistreatment rates should not be occurring with such predictable frequency in a country where nearly 100% of American birthers are attended to by a medical or childbirth professional and where most births take place in medical facilities, both of which are protective factors for maternal health outcomes (Bowser & Hill, 2010; Bohren et al., 2019; WHO, 2015; 2018; Salter et al., 2023). There is a missing link in the logic. Violation of Democratic Principles High rates of mistreatment in a country rich in medical technologies, providers and relative birth choice thus presents the paradox of mistreatment in American childbirth. This paradox forces a re-evaluation of how the pillars of democratic values on which the country rest are challenged in light of these phenomena. First, there is the sad irony of a strikingly robust medical care system, minimal medical treatment gaps, and high financial and practical resources, alongside a horrific and climbing maternal mortality rate. Second, the mistreatment of women in labor is particularly countercultural to American democratic values, “given a cultural emphasis on autonomy, gender equity, human rights, better working conditions for providers, and resources for training” it for lack of a better word, “should” not be happening here (Vedam et al., 2019). And yet it is. - - 33 There are myriad American cultural and systemic factors which may be interacting with American childbirth experiences to produce this paradox. These may include: a “pro-natalist” cultural and political disposition towards reproductive planning, the medicalization of childbearing, the normalization of increasing wealth disparity, American cultural attitudes towards women’s sexuality, implicit and explicit racism (systemic and structural racism) and the American model of health insurance and medical training within that model (Shabot & Korem, 2018; Chadwick, 2018; Athan, 2020; Bailey et al., 2017). Patriarchy, Politics and Power Dynamics: Consequences of Conservatism A culture of “American exceptionalism” in medicine has arguably contributed to this blind-spot around the flaws within our medical system (Vedam et al., 2017; Liese et al., 2021). Studies have found that the discrepancy in views between providers and patients of what is “medically-necessary” and subsequent feelings of disrespect and loss of autonomy are prevalent among United States birthing persons (Bowser & Hill, 2010). This is particularly true for Black and Latina women who were 50% more likely to report being treated poorly by a medical care provider due to a difference of opinion in her own medical care in labor (Declerq et al., 2010.) The problematic approach to women’s autonomy over their own health in America has been well documented across the legal, public health and psychological literature, evidenced by increasingly restrictive abortion rights and a paucity of evidence-based sexual health education across America (Shabot & Korem, 2018; Athan, 2020; Paltrow, Harris & Marshall, 2022). America’s views on women and the implicit gender discrimination in personal rights are clearly present in the experience of birth. Conservative cultural views towards women’s bodies introduce implicit bias into medical decision-making and impair women’s autonomy, even in the case of their own childbearing - - 34 (Chadwick, 2018). One example of this is a litigation regarding an unwanted surgery in birth by an unwilling and non-consenting patient (Hon. Genine D. Edwards, 2023; Brief for National Advocates for Pregnant Women as Amicus Curiae, 2016). The defendant (the hospital) argued that “pregnancy creates ipso facto immunity for any act deemed by the medical provider to be in the best interest of a fetus” essentially stating that the “doctor knows best” (Diaz-Tello, 2016). In essence, the state ruled that protecting a “potential life” was more valuable than honoring the decision of a “mentally competent and unwilling patient” thus repeating a pro-natalist pattern in medicine that values infant health over women’s health (Diaz-Tello, 2016; Athan & Reel, 2011). This example helps to illustrate how the American pro-natalist culture and de facto gender inequality that persists here, imbues and informs medical decisions deemed crucial, and helps to provide background on the cultural context of the paradox of mistreatment in America (Athan, 2020). In short: cultural factors reveal that it is not such a paradox after all. The United States is in fact arguably very high risk for mistreatment in childbearing due to social and cultural determinants. These gender dynamics and American cultural factors are of clinical importance, as recent studies on mistreatment in labor have identified that disrespect and abuse in labor differentially occur among American women by income status, racial and ethnic background and immigration status (Morton et al., 2018). Given the data demonstrating that the medical preferences, complaints and descriptions of pain by Black women in America are differentially interpreted by White doctors, and not responded to compared to their White American peers, the birthing room thus presents a robust intersectional environment in which implicit bias, systemic disparity and, violation of women’s basic human rights in labor are likely to occur. In this way, the act of - - 35 American birth is ripe for re-examination and disruption through culturally-informed, interdisciplinary participatory research. Customer is King: The Right to Sue for Malpractice Further, the power of the American citizen to hold medical institutions responsible for their actions via malpractice suits presents a double-edged sword to maternal health outcomes. While the power to litigate and hold providers accountable arguably empowers patients and mothers, it also breeds fear, anxiety and an over-abundance of caution among providers. Many American OB/GYNs live in fear of litigation. As such, medico-legal concerns are associated with high C-section rates as the cost of malpractice insurance increases (Barber, 2011). There is also a perception that C-sections are safer, informed by skewed risk calculations which perceive small risks to the fetus as worth mitigating at the cost of any risk to the mother (Lyerly et al., 2009). Obstetrics is the most litigated medical specialty in the United States (Liese et al., 2019). In lawsuits filed by women against their providers, it is “the amount of time from ‘decision to incision’” that is used to determine evidence for whether the providers acted to prevent infant or maternal morbidity or mortality fast enough (Liese et al., 2019). As such, it is easy to see how C-sections in America have become some of the highest in the world (Betran et al., 2016). The pressure placed on doctors to not hesitate to perform obstetric surgeries and to reduce even small risks to infant life, helps to illustrate why C-sections may be perceived by mothers to be extremely fast, non-consensual and traumatic. In short, doctors are incentivized to act fast which may be very scary for the patient. Hospital Deliveries, Doctor Deliveries: An American Phenomenon Further, America is one of the few high-resourced nations in the world where births routinely occur in a hospital-setting and are delivered by doctors rather than midwives (Betran et - - 36 al., 2016; Miller et al., 2016). Birth is a normal process not a pathology, and yet, one goes to a hospital for birth, where one goes to be treated for pathology. Birth can become pathological but is not inherently pathological. Further, American OB/GYNs are trained primarily in surgery, therefore they are more likely to use surgery to resolve an obstetric complication than other methods used by midwives, who do not train in surgery. This training model coupled with the culture of hospitals to treat acute presentation helps to illustrate why studies have found that American doctors tend to “overtreat” (Lyu et al., 2017). This may be accounted for by the ‘too much too soon’ theory and America’s “quick fix culture” found across other specialties in American medicine (Miller et al., 2016; Klein, 2004). Or, the tendency to overtreat in labor may reflect the afore-mentioned malpractice complaints in obstetrics, as described by the ‘unintentional harm’ theory of mistreatment in labor (Liese et al., 2021). One study examined providers’ perceptions of overtreatment in medicine and found that providers felt that 20% of treatments were unnecessary, with 80% of respondents endorsing fear of malpractice as the primary driver of this behavior (Lyu et al., 2017). This is a staggering finding. As such, increased exposure to obstetric interventions and overuse of interventions (that may be unnecessary) may also be a driver of mistreatment. To address this, the current study assessed the rate of surgical interventions and its interaction with mistreatment in order to account for these cultural drivers in childbirth outcomes. Insurance: A Private, For-Profit Healthcare Industry As detailed previously, giving birth in America is very expensive, particularly in New York City (Johnson et al., 2020). America’s birth outcomes, such as our high C-section rate, are often compared to the United Kingdom, which has similar demographics but better birth outcomes and less medical interventions (Betran et al., 2016; Miller et al. 2016). One explanation for this may - - 37 be the privatized healthcare system in the United States, as compared to socialized medicine under the National Health Service in the United Kingdom. Private hospital systems which are commonly for-profit in the United States and publicly traded, are necessarily incentivized to perform higher-cost interventions, even unnecessary ones. Of note, New York is the only state that prevents for-profit, publicly traded corporations from hospital ownership which limits the number of private hospitals but has been the subject of debate in recent years (Hammond, 2018 May). While practitioners may not believe in this model of medicine, they nonetheless find themselves constrained by and forced to conform to it. First of all, there is economic motivation; birth is a business and C-sections are expensive, they cost twice as much as vaginal deliveries. Studies have shown that in California, for example, women are 17% more likely to have a C-section at a for-profit hospital than a university hospital (which have the lowest rates) (Johnson, 2010; Barber et al., 2011). Doctors are often paid by the amount of births they perform in a shift; however, birth may progress slowly for one woman or quickly for another. The correlation between these two factors has been observed in hospitals whose doctors are paid the same amount regardless of their number of deliveries in a shift: they perform less C-sections (Johnson, 2010). However, within the privatized healthcare system, pit is uncommon to pay doctors baseline salaries regardless of number of deliveries performed per shift. Together, these forces increase the rates at which birth is artificially induced, lead to more unnecessary interventions, and creates great confusion among patients regarding what is necessary and what is not. This engenders a unique birthing culture where mistreatment may be built into a technocratic system, by way of implicit incentive towards profit, turnover, and intervention (Betran et al., 2016; Liese et al., 2021). - - 38 Measuring Mistreatment There have been few studies examining the phenomenon of mistreatment in labor in the United States. This is in large part owing to the dearth of scales validated for use in the United States, given the relatively recent attention paid to mistreatment in high-resourced regions. In fact, the most comprehensive review of childbirth perception scales found that, as of 2016, there existed only 36 well-validated measures of women’s perceptions of childbirth experience in the world. Of these, only 6 were developed and validated in (or adapted for) the United States (Nilvere et al., 2017). Of these, only three received a “quality” rating byway of the researcher’s evaluation of validity. Those scales that have been validated lack diversity in their sample (Hednett et al., 1987; Fawcett, 1992; Cranley et a., 1983), utilize measures not validated for use with diverse populations or have poor validation (Marvi et al., 1979), and many of them focused on a single component of birth experience (for example: tokophobia, mode of birth, trauma) (Wijma, Wijma, & Zar, 1998; Pierre et al., 1994). None of these examined the role of race on mistreatment, and none utilized maternal perspectives or participatory action research (PAR) methodologies to develop the scales. Globally, the scales that have been developed with quality validation methodology are not validated for use in the United States and had similar aforementioned limitations in terms of dimensions of mistreatment and limited racial diverse in their samples (Carquillat et al., 2017; Denker et al., 2018). Valid Measures of Mistreatment: MORi and MADM Following the Nilvere review, Vedam and colleagues developed and validated the most comprehensive measures of maternal mistreatment for use in the United States to date: The Mothers on Respect Index (MORi) and the Mother’s Autonomy in Decision Making scale - - 39 (MADM) (Vedam et al., 2017; Vedam et al., 2017). These are the only scales developed to assess mistreatment along the lines of the WHO and Bohren’s “7 Dimensions of Mistreatment” (Vedam et al., 2019). Vedam et al. utilized these scales to complete the most comprehensive study to date on the maternal experience of mistreatment in the United States: The Giving Voice to Mothers Study (2019). For this, they utilized PAR whereby birthing people themselves developed and validated the scales, making it arguably the most equitable and justice-oriented tool for this purpose. The Giving Voice to Mothers study had a sample size of 2,300 women derived from all 50 states, included ethnic and racial minorities, and also represented both community-based and hospital-based births. They developed and adapted a 218-item survey that included their MORi and MADM scales as well as an adapted Perceived Racism Scale, and other participant and expert developed items to assess respect, control, abuse and autonomy in labor (Vedam et al., 2019; Green, 1995). Their study was not representative and in fact had an over-representation of vaginal births compared to the national average, which is of import because spontaneous vaginal births are correlated with less mistreatment and disrespect in labor. Even so, they found that 17% of women experienced mistreatment in childbirth; they found higher rates for women of color, for hospital births, for Cesarean births, women with a partner of color (black), low-income women, and those with health complications (Vedam et al., 2019). Unexpected medical/obstetric interventions and patient-provider disagreements compounded the experiences of mistreatment, per the study. As such, there is an opportunity to build upon this work and use the most well-validated, adapted and comprehensive scales to date in order to comprehensively examine mistreatment in American childbirth. - - 40 CHAPTER III: CURRENT STUDY Significance of this Work The present study is of significance to both the maternal health and mental health fields, and has implications for public health research and public policy. The most comprehensive study of maternal birth experience in the United States to date was conducted in 2019 and found a 17% rate of mistreatment in perinatal experience (Vedam et al., 2019). However, this study did not assess mental health outcomes of mistreatment in labor. Further, the study found preliminary evidence that medical pregnancy risk complications (i.e. BMI>40, gestational diabetes) did not sufficiently account for significant differences in birth and neonatal outcomes when holding for race, income-level, and other sociodemographic risk factors. Their data found that mistreatment was the only significant difference in negative birth outcomes when comparing White and Black American women (Vedam et al., 2019). These findings suggest that mistreatment may not only be higher among these groups, but that the maternal experience of mistreatment in childbirth may act as a moderator between risk factors and maternal health, and mental health outcomes. As such, it would be of incredible clinical, psychological and medical significance to understand how mistreatment may contribute negative perinatal and psychological outcomes, otherwise accounted for by complicated health conditions. Further, the present study aimed to discover leverage points for addressing New York City’s high maternal mortality and severe maternal morbidity rate, which disproportionately impacts women of color, and to provide translational research on the role of perinatal birth experience on perinatal mental health, towards the collaborative care of women in New York City - - 41 (NYC Health, 2021). In this sense, the current researcher posits that the study has the potential to identify maternal mistreatment in labor as a potential syndemic here in New York City (Lancet, 2017). Of further significance, the aims of the present paper are in line with current global public health initiatives in the areas of maternal health, mental health, human rights, and health equity. This research directly and indirectly addresses the Millennium Development Goals (MDG) of reducing maternal mortality and the WHO’s Sustainable Development Goals of Gender Equality and Good Health and Well-being (United Nations, 2018). Aims & Hypotheses The present study aimed to establish the rates of mistreatment in childbirth in a sample of women in New York City, for whom mistreatment occurs, to determine possible moderators of the quality of childbirth experience, and to understand the impact of childbirth experience on maternal mental health outcomes. Further, the study aimed to assess participants’ own perceptions, understanding, and explanations of mistreatment during their labor towards identifying patterns and themes across these experiences. The specific aims of the study are accompanied by hypotheses for quantitative aims and exploratory research questions for qualitative aims, below. Aim 1: Mistreatment and Mental Health To measure and statistically describe the frequency and forms of mistreatment and common mental disorders in maternal health settings (hospitals) in New York City, to establish the current rates of these phenomena among a diverse cohort of birthing women. - - 42 Hypothesis 1a. As measured by the Mothers on Respect Scale (MORi), the majority of participants (>50%) will endorse the experience of receiving Moderate and Low Respect in prenatal care and labor on the index pregnancy. Hypothesis 1b. As measured by the Mothers Autonomy in Decision Making Scale (MADM), the majority of participants (>50%) will endorse the experience of having Moderate and Low Patient Autonomy in prenatal care and labor on the index pregnancy. Hypothesis 1c. Rate of perceived racism, as measured by an adapted short form of the Perception of Racism Scale, will be common (>50%) in the sample. Hypothesis 1d. The rate of postpartum depression in the sample, as measured by the Patient Health Questionnaire (PHQ-9) will reflect the current estimates for New York City women, which is ~15% per CDC estimates for postpartum depression (2020), with a <10% rate of suicidal symptoms in the sample. Hypothesis 1e. The rate for posttraumatic stress related to recent childbirth (CB-PTSD) as measured by the PCL-5, will be about 5%, per current estimates. Aim 2: Moderators of Mistreatment To determine which participant-level variables may be moderating mistreatment in labor, in order to identify potential “risk” and “protective” factors of mistreatment in maternal care. - - 43 Hypothesis 2a. Scores on MORi and MADM will be lower for women who are Black, Latinx and Asian as compared to Caucasian women; for women who are uninsured or on Medicaid, as compared to women who have private health insurance; for women who did not have a birth plan as compared to women who did; for women who have an emergency C-section or unwanted interventions, as compared to those who do not; for primiparous mothers compared to multiparous mothers; and for women who do not have a birth companion, as opposed to those who do, when holding for other variables. Hypothesis 2b. The impact of racial identity on MORi and MADM scores will be moderated by perception of racism, “high-risk” pregnancy factors (i.e. gestational diabetes), and insurance status. Hypothesis 2c. The impact of emergency C-section, unwanted intervention, on MADM and MORi will be moderated by presence of a birth plan, race, and childbirth education. Aim 3: Moderators of Maternal Health and Mental Health Outcomes To assess the impact of mistreatment on the health and mental health outcomes of women and to explore how different levels of mistreatment in perinatal care and labor may act as moderators between sociodemographic variables (i.e. race, income-level) and mental health (i.e. postpartum depression), as well as perinatal medical outcomes of birthing persons (i.e. emergency-C-section, unwanted intervention, etc.). Hypothesis 3a. PHQ-9 depression scores will be significantly negatively correlated with MADM and MORi scores. - - 44 Hypothesis 3b. PCL-5 posttraumatic stress scores will be significantly negatively correlated with MADM scores but not significantly correlated with MORi scores. High PCL-5 scores will be the most highly correlated with low MADM scores, when compared to PHQ-9 scores. Hypothesis 3c. Participants who endorse any mental health history or at least three ACE items, will report lower MADM and MORi scores than those who do not. Hypothesis 3d. MADM and MORi scores will be significantly negatively correlated with the Mother Infant Bonding Scale (MIBS). Hypothesis 3e. MADM and MORi scores will act as a moderators between maternal mental health history (i.e. any pre-conception history of depression) and subsequent postpartum mental illness. Hypothesis 3f. MADM and MORi scores will act as moderators between childbirth variables (i.e. presence of birth plan, emergency C-section), and postpartum mental health. Aim 4: A Model of Mistreatment To fit a model of mistreatment by assessing how well significant variables explain the relationships between mistreatment and variables of interest using path analysis, in order to evaluate the correlational data between the predictor variables, moderator variables, and outcomes explored in Aims 2-3. - - 45 Aim 5: Qualitative Perceptions of Mistreatment To explore the qualitative perceptions of mistreatment and respect and autonomy in labor and thematically describe the macro and micro level systemic factors that may act as “drivers” of mistreatment or that are perceived by women as contributing to mistreatment in maternal health settings. The research questions for Aim 5 may be organized into five domains: patient birth stories and narratives; factors participants believe impacted their birth outcomes; preparation, knowledge, and expectations before the childbirth; perceptions of care and treatment; and, emotional and psychological impacts of their birth experience. Please see Appendix A for a complete list of research questions for the qualitative aims. Aim 6: Methodological Triangulation To triangulate and integrate the statistical quantitative and thematic qualitative findings in order to illustrate, explain, and robustly outline the rates, correlates and moderators of mistreatment in labor, supported by thematic quotes and significant statistical results, to form an illustrative and descriptive representation of the experience of mistreatment. - - 46 CHAPTER IV: METHOD Setting and Participants Inclusion & Exclusion Criteria The present study aimed to recruit 200 survey participants and 8 qualitative interview participants, over an eight-month period. Inclusion criteria encompassed: adults (18 y/o) who have given birth more than two weeks prior to data collection and less than 12 months prior to data collection. To be included, participant must have given birth at a New York City hospital, in one of the five boroughs. Exclusion criteria encompassed: minors, non-birthing persons, nulliparous mothers (foster parent, adoptive parent), persons who have given birth more than 12 months prior to data collection, and those who gave birth at home or at a birthing center. The decision to assess mistreatment within a temporal range in the postpartum period, rather than at one specific time-point reflect the time periods in which the mistreatment questionnaires were validated for use (Vedam et al., 2017; Vedam et al., 2017; Green, 1995). Participants also needed to possess facilities with English language and some technological literacy and have access to a computer or mobile device with internet access. Finally, all eligible responses were required to pass the fraudulent-response detection system, outlined below, for compensation and study inclusion. Sampling The study employed purposive and snowball sampling to recruit participants. Participants were recruited via online platforms and in-person advertisement. Flyers advertising the study were posted to Facebook groups, direct-messaged to parenting influencers and birth workers on Instagram via the study’s public Instagram account advertising the study, emailed to pediatricians, midwives, doctors and doulas, and posted to online platforms including Mommy Poppins Blog, Park Slope Parents, NYC Dads Meet-up Group and shared within the Teachers College, Columbia - - 47 University and Lenox Hill Hospital listservs. Digital flyers were also disseminated to birth support services including Manhattan Birth, Ancient Song Doula Services, and the Childbirth Education Association of New York. Physical flyers were handed out across the five boroughs at places of worship, outside of daycare centers, OB/GYN offices, and women’s health clinics who gave the study permission to canvas at their site. Please see Appendix C for recruitment flyer. Data Collection Quantitative Data To collect demographic information, survey questionnaires, and mental health screeners, participants completed a 20–30-minute online Qualtrics survey which contained yes-or-no and multiple-choice questions. Participants were compensated for their time with a $15 Visa Gift Card which was delivered to the participant digitally, upon completion of the survey and assessment of the validity of the survey session. Participants were provided with mental health resources following survey completion due to the potentially distressing nature of the survey questions. Quantitative data was downloaded from Qualtrics into Microsoft Excel for cleaning and organization, then uploaded into Intellectus software for analysis. Qualitative Data Survey participants were given the opportunity to participate in a qualitative interview. Participants who consented to this option were contacted by the research team. Qualitative interviews occurred via a HIPPA-compliant Zoom account. Data were collected using 1-hour, semi-structured interviews conducted by a PhD-level researcher with clinical and research expertise in perinatal psychology and healthcare systems as well as a trained masters-level researcher. Interviewers followed the same semi-structured interview guide, which was organized - - 48 to capture key domains, detailed in Appendix G. Interviews were audio-recorded, hand-transcribed, and then coded using NVIVO software. Procedure The study utilized a cross-sectional design and mixed-methods approach to data collection and analysis. First, the study collected sociodemographic, medical, mental and birth history information via a 41-item self-response questionnaire. Participants were subsequently administered validated measures of respect, autonomy and mistreatment in childbearing and perinatal care. Finally, validated self-report questionnaires measuring mental health outcomes were administered via the online survey. Survey participants who were interested in being interviewed about their birth experience engaged in semi-structured interviews following their survey completion. Data storage, collection and security adhered to the Teachers College IRB protocol for research with the population of interest (ID#: 22-235). Measures This cross-sectional study aimed to assess the lived experiences and quantitative relationships of maternity care and mental health in the United States. The study aimed to quantify the prevalence of mistreatment by race, socio-demographics, mode of birth, place of birth, and context of care, and describe the intersectional relationships between these bivariate variables and the experiences of mistreatment as measured by the following scales. Survey Questions The first portion of the online questionnaire encompassed a 41-item yes-or-no or multiple choice-format questionnaire to obtain information regarding predictor and indicator variables. - - 49 These questions are based on data suggesting that these factors may influence the outcome of birth, the experience of hospital birth, the perception of birth, and mental health among birthing persons. The survey questions were designed to capture four domains: (1) childbirth information, (2) socio-economic demographics, (3) medical/health history, and (4) mental health history. These data were treated as independent and moderator variables, within the statistical models. Survey questions capturing the four domains and the respective response options may be found in Appendix B. These data were collected using single item self-report via anonymous internet survey, therefore neither medical nor psychological treatment records were collected or reviewed. Following this, participants were administered eight questionnaires, detailed below. Questionnaires were formatted for easy administration within the online survey. Questionnaires aimed to collect quantitative data within the domain of mental health history, to provide measures of mistreatment, and to provide measures of current mental health functioning. The first two questionnaires inquire about mistreatment (MORi, MADM, detailed below) and were evaluated as both moderator and dependent variables. The third questionnaire assesses the number of negative childhood experiences (ACE, detailed below) and is categorized into the domain of mental health history within the survey, treated as an independent variable. The final five questionnaires (the PHQ-9, the PCL-5, the MIBS, and PRS, all detailed below) collected mental health data and were evaluated as dependent and moderator variables. The Mothers on Respect Index Experiences of respect in childbirth is one measure of mistreatment in labor (Bohren et al., 2015). Therefore, the current study utilized the Mothers on Respect Index (MORi) to measure the perceptions and experience of respect and mistreatment in maternal care settings (Vedam, Stoll & Rubashkin et al., 2017). The MORi is one of the few measures of perceptions of mistreatment in - - 50 childbirth settings that is validated for use in the United States and that was validated using a racially and economically diverse sample of participants (Vedam et al., 2017). The measure was developed for use in Canada in maternity care settings including hospitals birthing centers, and prenatal care centers; it was then piloted and validated for use in the United States following a comprehensive participatory action research (PAR) informed validation study that sampled from all 50 states (Vedam et al., 2019; Vedam, Stoll & Rubashkin et al., 2017). Overall, the screener was demonstrated to have good internal consistency and was replicable across three samples, with a Cronbach’s alpha of a = 0.94 for the U.S. sample. Further, the item-to-total correlations and factor loadings showed that the scale measured a single construct: the nature of respectful patient-provider interactions, and the impact of these interactions on patients’ feeling of comfort, their behavior, and their perception of racism and discrimination (Vedam, Stoll & Rubashkin, 2017). The 14-item scale is divided into three sections. Together, these measure perception of respect during childbirth and prenatal care using a 6-point Likert scale from “Strongly agree” to “Strongly disagree.” In Section A of the MORi, respondents are provided with the following prompt: “Overall, while making decisions about my pregnancy or birth care…” Response options in section A include: “I felt comfortable asking questions” and “I chose the care options that I received.” In Section B of the MORi, respondents are provided with the following prompt: “During my pregnancy I felt that I was treated poorly by my doctor or midwife because of…” Response options include: “My sexual orientation or gender identity” and “My type of insurance or lack of insurance.” Finally, respondents are provided with the following prompt in Section C: “During my pregnancy I held back from asking questions or discussing my concerns because…” Response options include: “My doctor or midwife seemed rushed” and “I thought my doctor or - - 51 midwife might think I was being difficult” (Vedam et al., 2017). Responses to Sections B and C of the MORi are reverse scored. Higher scores indicate higher levels of respect, such that scores <14-31 indicate Very Low Respect, 32-49 indicate Low Respect, 50-66 indicate Moderate Respect, and >67-84 indicate High Respect. The Mother’s Autonomy in Decision-Making Scale (MADM) The Mother’s Autonomy in Decision-Making Scale (MADM) was utilized to measure a mother's sense of control and autonomy in her birth process (Vedam, Stoll & Martin, 2017). Scale development was patient-informed and used participatory action research (PAR); samples were racially, economically and medically diverse (Vedam, Stoll & Martin et al., 2017; Vedam, Stoll, Taiwo et al., 2019). Overall, the measure demonstrates good consistency and validity. The item-to-total correlations were replicable across three samples and the Cronbach’s alphas were all above a =.90 for the Canadian and British Columbia samples. Content validation of the MADM was conducted in 2019 for use in the United States, which was found to have good validity (Vedam, Stoll, Taiwo et al., 2019). The 7-item scale measures a single construct that assesses one domain of mistreatment: autonomy in decision making regarding maternity care. The questionnaire includes questions such as: “I was able to choose what I considered to be the best care option” and, “My doctor or midwife respected my choices.” Respondents rank responses on a 6-point Likert scale from “Completely disagree” to “Completely agree.” The possible range of scores is 7-42, where scores <7-15 indicate Very Low Patient Autonomy, scores 16-24 indicate Low Patient Autonomy, 25-33 indicate Moderate Patient Autonomy, and scores of 34-42 indicate High Patient Autonomy. Higher scores indicate the patient had more opportunities to take an active role and lead the decision-making in her own care (Vedam et al., 2017). - - 52 Adapted Perceptions of Racism Scale An adapted version of the Perceptions of Racism Scale was used to measure perceptions of racism and discrimination in maternity care and childbirth. The scale was originally developed and validated for use with birthing persons to evaluate the perceptions of racism in labor and delivery among African American women (Green, 1995). The original measure is a 20-item scale that measures perceived racism on a 4-point Likert scale and has good reliability with Cronbach’s alpha of .88-.91 (Green, 1995). The multidimensional measure conceptualizes racism as occurring in two domains: racism in the healthcare system and general societal racism, which it measures as total lifetime experiences of racism (Atkins, 2014; Green, 1995). Racism is defined as when the respondent perceives treatment, experiences, or attitudes based on the perception of differential treatment and this perception impacts their thoughts, behaviors, attitudes or functioning (Atkins, 2014). An adapted version of the scale was validated for use with all study populations (Vedam, Stoll & Taiwo et al., 2019). The present study will utilize this version of the scale, which contains questions that evaluate perceived discrimination from care providers, disrespect from care providers due to race, and perceptions of unfair treatment due to race, ethnicity or cultural heritage (Vedam, Stoll & Taiwo et al., 2019). The Patient Health Questionnaire, 9-item Scale The Patient Health Questionnaire (PHQ-9) was used to measure depression. The PHQ-9 was selected for multiple reasons (Kroenke, Spitzer & Williams, 2001). First, it provides a measure of suicidal ideation and self-injury which addresses the literature suggesting that postpartum suicidality is higher than previously reported. Further, it is a commonly used depression measure in medical settings including in New York City hospitals, and therefore fits within the current - - 53 medical framework in which the data are being collected. Finally, the PHQ-9 has been validated for and extensively used with perinatal populations in the United States, with the most recent systematic study finding a reliable pooled specificity (as defined by an area under the curve of .89, on average, indicating excellent diagnostic validity) (Wang et al., 2020). Finally, the PHQ-9 contains the three DSM-5 criteria for diagnosing depression (cut-off score of 8-11), allowing researchers to not only detect likely depression but to diagnose based on item-specific responses (Manea, Gilbody & McMillan, 2012; Kroenke, Spitzer & Williams, 2001). While the PHQ-9 was not originally developed for use with postpartum populations, studies have found that if it is given more than 2 weeks after the known emotional reactivity period postpartum, it has similar validity and reliability as the Edinburgh Postpartum Depression Scale (EPDS) (Wang et al., 2020; Yawn et al., 2009; Cox, 1987). Further, the EPDS does not contain an explicit suicidality question rendering it less applicable to the aims of the current study, despite its apparent relevance as a postpartum depression screener. The PTSD Checklist for DSM-5 The study administered the PTSD Checklist for DSM-5 (PCL-5) to measure posttraumatic stress related to recent childbirth (CB-PTSD) (Weathers et al., 2013). The PCL-5 is a 20-item self-report measure that was developed to measure likely PTSD based on the DSM-5 criteria, and studies have found that it is a reliable and valid measure of posttraumatic stress as compared to clinician assessment with Cronbach’s alpha of a =.90. Cutoff scores above 30 on this measure indicate probable PTSD, however calculation of DSM-5 cluster criteria as compared to respondent item endorsement is required for clinical diagnosis. Further, the PCL-5 is a commonly employed measure of CB-PTSD in clinical and non-clinical samples (Bovin et al., 2016; Mayopoulos et al., 2021). However, given that the present study is cross-sectional in design, it is not possible to - - 54 establish whether the PTSD was present prior to the childbirth, therefore the triggering event may be uncorrelated with the birth. The Adverse Childhood Experiences Scale (ACE) The study administered the Adverse Childhood Experiences Scale (ACE) to account for adverse and traumatic experiences in childhood, which have been found to impact adult psychological functioning as well as medical outcomes in adulthood. The ACE is a ten-item self-report questionnaire that is administered to adults, which measures childhood experiences of emotional, physical and sexual abuse, neglect, and household dysfunction (Felitti et al., 1998). The ACE was originally developed by researchers examining heart disease and obesity who observed that many patients with health problems also had histories of physical and sexual abuse. Since this discovery, many studies have demonstrated the correlation between ACE scores and adult experiences of depression and negative medical outcomes (Felitti et al., 1998; Chapman, 2004; CDC, 2016). The ACE has ten questions which ask the patient to determine if, before age 18, they experienced instances of emotional abuse, physical abuse, molestation, a primary caregiver with mental illness, a primary caregiver who was incarcerated, etc. Each yes-or-no question is scored 1 or 0 respectively. There is a possible total score of 10 (Felitti et al., 1998). A score of 0 on the ACE means there was no exposure to any of the listed adverse childhood experiences, whereas a score of 10 means that the person had full exposure. The higher the score, the more likely the person is to experience poor health outcomes (acestudy.org). People who have endorsed six or more questions on the ACE are at a significantly increased risk for poor health outcomes as adults, including depression and suicide attempts (Felitti et al., 1998; CDC, 2016). - - 55 The Mother Infant Bonding Scale (MIBS) To assess the impacts of mistreatment in birth on parenting behavior, the study administered the Mother Infant Bonding Scale (MIBS). The measure is a 25-item Likert style questionnaire that assesses maternal-infant bonding by asking the mother to rate the degree to which she feels different emotions towards her infant, including loving, resentful, neutral, joyful, dislike, protective, disappointed, and aggressive (Taylor et al., 2005). Each item is measured on a six-point Likert scale from ‘always’ to ‘never’ which are scored on a scale from 0-5 respectively (Klier, 2006; Brockington, Fraser & Wilson, 2006). There is a possible score of 0 to 24, with a higher score indicating more impaired maternal bonding or infant rejection (Taylor et al., 2005; Van Bussel, Spitz & Demyttenaere, 2010). The MIBS possesses good factor loading and reliability and has been validated for use with the general population (Taylor et al., 2005; Kumar et al., 2007; Yoshida et al., 2012). Establishing Validity of Online-Administered Measures In response to the COVID-19 pandemic, the data collection for health science research has largely moved online to increase accessibility. As a result of this, there is a greater threat to validity of the data collected through online surveys (i.e., Are the respondents who they say they are? Are they truly eligible?). This has become a major threat to the validity and thus the establishment of fact in health and psychology research. In fact, one recent study found that as many as 96% of online respondents may be classified as fraudulent or “bots” (Pozzar et al., 2020). As such, it is imperative that all online data collection undergo rigorous bot-detection and operate from the assumption that a greater proportion of responses are fraudulent than non-fraudulent. Given that - - 56 the study compensated all eligible participants, the study was particularly vulnerable to false or random responding, the research team took steps to protect the validity of the study. Detection of “bots” and fraudulent responses Studies have found that implementing measures including duplication detection of timestamp and duration of survey, free-response consistency checks, attention checks utilizing logic questions, and assessing response times can detect fraudulent responses (Zhang et al., 2022). Further effective measures include documenting the time at which the survey was completed, screening open-ended questions, targeted recruitment, limited distribution of the survey link, and comparing psychometrics of reverse scored items within the same survey session (Storozuk, 2020). To ensure the validity of the data and to reduce the likelihood of bots or false responding, the research team implemented a series of evidence-based fraudulent-response detection methods aimed at rapidly identifying and eliminating fraudulent or bot-generated responses. To prevent participant false responding on the eligibility screener questions, the full eligibility criteria were not detailed on recruitment materials. This helped to ensure that potential participants who meet the eligibility criteria are doing so without prior knowledge of it. There were several attention and consistency checks throughout the survey. Completion time for the survey was evaluated by the research team. The length of the survey was estimated between 20 and 30 minutes, therefore surveys completed in a short amount of time (<15 mins) may indicate random responding or bot interference. Further, completion time for each question was evaluated as well. The temporal length of question responding was assessed to ensure it was a feasible pattern (i.e., >5 seconds per question; not spending 40 minutes on the final question and 1 second on the rest). This helped to reveal bots that were coded to meet the minimum duration of survey. - - 57 Further, completion of the survey by only one person at a time was measured by email provided for compensation and by IP address. Participants who provided the same email twice for separate survey sessions, or, survey sessions that were recorded from the same IP address were rendered ineligible for compensation. Further, honest response patterns, as indicated by the absence of the following indicators of dishonest survey responding, were assessed: responding only ‘yes’ or ‘strongly agrees’ (respondent acquiescence bias), responding with only neutral answers (N/A), and “straight lining” or responding with the same point for every scale. The study implemented a pointed system to assess the results of these measures. Each bot-detection item was worth a certain number of points, such that if a survey response earned more than 4 points, it was not included in the study. Minor infractions (such as failing one attention check) was only worth ½ point, while major indicators of fraudulent responses, such as the exact same free response as another responder was worth two points. This point system built in the possibility for human error, protected valid responses from false negative errors, and also created an evidence-based approach to eliminating responses without violating the rights of participants. Therefore, this system allowed the research team to provide feedback to both the survey taker and IRB on why they were not eligible for compensation and avoided bias in researcher evaluation of response validity. Data Analysis Study Aim 1: Mistreatment and Mental Health Frequency Data The analysis aimed to measure and statistically describe the observed frequencies and forms of mistreatment in maternal health settings, and the observed frequencies and forms of - - 58 mental illness among the sample. To establish the prevalence of mistreatment, the study measured the average score on the index measures of MORi, MADM and PRS, the percent respondents who indicated above cut-off on the index measures, as well as item-level prevalence per measure. The study also established the prevalence of mental illness among this cohort sample by measuring the percent of participants who were at or above cut-off score for the psychological measures (PHQ-9, PCL-5, ACE). Statistical software was used to evaluate measures of central tendency and variance for the total index scores (Nicholas, 1990). These measures provide the field with a preliminary indication of the prevalence of this phenomenon within a sample of women and helped to establish the baseline mental health characteristics of the sample, before turning to more in-depth analyses. All quantitative analyses presented in Aims 1, 2, 3, and 4 were conducted using Intellectus Software, a statistical software package, and Microsoft Excel. Study Aim 2: Moderators of Mistreatment MANOVA The primary goal of this analysis was to explore for whom mistreatment is occurring and to identify what (if any) effects arise from women’s mistreatment in labor. To do this, the study identified what patient-level, categorical variables act as moderators of mistreatment in labor, as defined by scores on the MORi and MADM. To test the main effects, the study employed a multivariate analyses of variance (MANOVA), which is a statistical technique used to explore the effects of independent categorical variables, on multiple continuous dependent variables as modeled by Wilks’ lambda (Tabachnick & Fidell, 2011; French et al., 2008). Regression assumptions of OLS, normality, and heterogeneity of variance were assessed prior to running the regressions (Berry, 1993). - - 59 ANOVAS To test the interaction effects between the dependent and outcome variables, moderation effects were evaluated (Andersson et al., 2014; Baron & Kenny, 1986). The study conducted several analyses of variance (ANOVA) on each moderator variable, detailed in the hypotheses, and each of the dependent variables which are the MORi and MADM scores (Fairchild & MacKinnon, 2009). Then, the study ran a multiple regression to test the interaction coefficients (Aiken & West, 1991). To examine Hypothesis 2c, for example, the study regressed “unwanted intervention” (categorical independent variable) on the MADM score (continuous dependent variable) then regressed the “presence of birth plan” (categorical independent variable) on MADM score and tested for the interaction effects. Mediation effects were not examined due to the impossibility of establishing temporal precedence in cross-sectional designs (Busk, 2005). There were multiple ANOVAs run on each dependent variable MORi and MADM, per each hypothesis. To correct for the Type I error rate, the study utilized a Holm (or, Holm-Bonferroni) correction to correct for the family wise error rate when running multiple comparisons on the same dependent variable (Holm, 1978). This was chosen because the data did not meet the criteria for a Tukey HSD test, which requires the same level of variance between groups (Abdi & Williams, 2010). Further, the Holm method has been found to be more powerful than the Bonferroni correction. Moderation Analyses Following this, moderation analyses were examined and interpreted using the model presented by Aiken and West whereby interaction terms that are greater than zero are interpreted as moderating the relationship between the dependent and independent variable (1991). Following - - 60 this, the interactions were then plotted to determine the regression slope values (Fairchild & MacKinnon, 2009). Aim 2. examined the moderating effects of patient-level demographic, medical, and psychological variables that may act as risk or protective factors for independent variables historically associated with poor childbirth experience, for the purposes of potentially engendering new theoretical insights to the field (Andersson et al., 2014). Utilizing moderator variables in hypothesized models necessitates theoretical reasoning or justification; the theoretical justification for this present aim is explored in the Background section, namely the high rate of correlation between demographic variables and poor birth experiences and the subsequent psychological sequelae. Further, preliminary evidence that these relationships are not fully explained by correlational modeling, suggests the presence of a moderator or mediator variable within the birth experience (Andersson et al., 2014; Vedam et al., 2019). Study Aim 3: Moderators of Maternal Health and Mental Health Outcomes MANOVA First, to assess the correlations between mistreatment and perinatal mental health outcomes of women, the study explored how different levels of mistreatment in perinatal care and labor may relate to mental health and health outcomes of birthing persons. To do this, the study conducted several Spearman correlations. Then, to test the main effects, the study utilized a multivariate analysis of variance (MANOVA) to explore the effects of independent categorical variables, on multiple continuous dependent variables (Tabachnick & Fidell, 2011; French et al., 2008). Regression assumptions of OLS, normality, and heterogeneity of variance were assessed prior to running the regression (Berry, 1993). - - 61 ANOVAS In this model, the mistreatment variables (MORi and MADM total index scores) were treated as moderator variables to determine the interaction effects between independent categorical variables and patient-level outcome variables including scores on mental health indices, C-section status, and medical interventions. To do this, the moderator variables were converted into categorical variables and split into four levels, and then the study conducted several analyses of variance (ANOVA) on each moderator variable and each of the three dependent variables (Fairchild & MacKinnon, 2009). As with Aim 2., the study corrected for the large number of effect sizes examined per dependent variable by utilizing a Holm correction (Holm, 1978). Moderation Analyses Then, the study ran a multiple regression to test the interaction coefficients (Aiken & West, 1991). Moderation effects were interpreted using the model presented by Aiken and West whereby interaction terms that are greater than zero, are interpreted as moderating the relationship between the dependent and independent variable (1991). Further, the interactions were then plotted to determine the regression slope values (Fairchild & MacKinnon, 2009). Study Aim 4: A Model of Mistreatment Path Analysis To develop and evaluate a cross-sectional model for mistreatment in labor, the study conducted a path analysis, a form of structural equation modeling (SEM). Measured variable path analysis was used to evaluate the significant relationships between predictor and moderator variables, with the outcome variables, following the findings of Aims 2 and 3 (Ralph & Gregory, 2019; Loehlin, 2004). - - 62 Path analysis lends itself to the present analysis because it provides an assessment of fit between correlational data of latent variables, and visually depicts the correlations and regressions within and between variables to render a systemic statical understanding of multiple variable relationships (Ralph & Gregory, 2019). Further, path analysis is appropriate for determining correlation and covariance among variables for single observation events; it allows for the testing of multiple models and functions to disprove proposed causality within models, lending it to the present analysis which is correlational in nature (Loehlin, 2004; Streiner, 2005). This allowed for the conceptualization and re-specification of the relationships explored in Aims 2 and 3. Given the high number of variables explored in the study, a path analysis allowed the study to explore which variables fit the cross-sectional model to explain the strength of the relationships between sociodemographic factors and mistreatment, risk factors and mistreatment, and the moderating relationships therein to elucidate a theory-driven, statistical model of mistreatment in labor. Study Aim 5: Qualitative Perceptions of Mistreatment Qualitative data were collected via audio-recorded interviews which were then hand-transcribed by the research team. The study utilized a team-based, qualitative coding method to develop a codebook, organize and code the data, following which coded data were analyzed to identify themes. Coders were trained in thematic qualitative coding by the lead researcher and were trained to use a uniform approach in the NVIVO software. Team-based codebook development requires intellectual diversity as well as shared theoretical knowledge in order to maximize thematic emergence and minimize false group consensus. As such, the coding team was composed of individuals who had shared theoretical expertise, possessed a diversity of - - 63 perspectives within the subject matter, and who had consistency in approach and training. The qualitative methodology employed is explained below. Codebook Development The study employed a stepwise, Inter-Coder Reliability (ICR)-led, codebook development approach which provides a robust method for establishing qualitative reliability (O’Connor & Joffe, 2020). The codebook development, coding and analysis utilized a combination of a priori and inductive reasoning methods informed by the current literature on mistreatment in conjunction with the emergent data arising from the transcripts themselves (MacQueen et al., 1998; Bohren et al., 2015). Because mistreatment is a phenomenon that has already been studied and described in the literature, this combination of inductive and deductive methods is the most appropriate approach for the current study, as it allows the research to build upon existing theory and to add new findings to this body of work to further define this phenomenon (MacQueen et al., 1998). To begin the process of codebook development using both inductive and deductive reasoning, the head researcher developed an initial code list derived from the current theory of Boren’s seven dimensions of mistreatment in maternity care (2015). This first codebook iteration leaned heavily on the use of etic codes, or those that contain objective or pre-established jargon and theory. This list was then reviewed by the full research team, where codes were added and removed and definitions were created (MacQueen et al., 1998). Then, as each of the eight transcripts were coded and re-coded, the codebook was edited to reflect emic codes, which utilize the respondents’ own words and definitions based on new information arising from the transcripts (MacQueen et al, 1998). This approach allowed the participants' own words to guide the codebook development while also building upon known and proven theory in this area of research. - - 64 The team utilized line-by-line coding to develop the codebook, which enabled the codebook and subsequent application of codes to be directly fed and defined by the data (Williams & Moser, 2019). Team-based discussion and hand-reconciliation of coding differences was employed to conduct the codebook development process (McLellan & MacPhil, 2008). Once the codebook reached saturation (i.e., when no new codes were added or emerged), and once the codebook had an overall intercoder agreement level of k >.60, the codebook was then applied to the coding of the transcribed qualitative data. Coding Method and Process The same stepwise, iterative approach used to develop the final codebook frame was then applied to the coding of the transcribed data. Coders coded full transcripts individually then discussed their coding results with the group in weekly team meetings, after which changes were made as necessary to the codebook definitions and structure or to the coding approach of individual coders. This process occurred over multiple codebook iterations and multiple rounds of coding the same transcript (Hruschka et al, 2004). To avoid what MacPhail calls “iterative convergence,” the study utilized a process whereby “agreement in coding is based on numerous discussions of specific transcripts” and coder divergence is “hand reconciled” (McLellan & MacPhail, 2008). Each transcript was coded only once until new codes emerged in the codebook development, at which point the transcript was re-coded utilizing the latest iteration of the codebook. This iterative, “cyclical problem-solving” approach allows for a more rigorous coding method and ultimately a refinement of the codebook structure that is emergent from the data (Morgan & Nica, 2020). Establishing Reliability. Reliability statistics provide qualitative researchers with the tools to identify weaknesses in the coding process, discrepancies between coders, meaningful - - 65 differences in the training of coders, and illuminate necessary changes in codebook structure throughout the iterative process (Neuendorf & Kumar, 2015). To establish reliability of the codebook frame and coding process, the study utilized Intercoder Reliability (ICR) to statistically measure the degree of agreement between different coders, regarding how the same line of data should be coded (O’Connor & Joffe, 2020). Cohen’s Kappa. ICR was measured using Cohen’s kappa, with values ≤ 0 as indicating no agreement and 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement (McHugh, 2012). Cohen’s Kappa coefficient is a statistic that indicates the degree of agreement between two raters and provides a statistical measure of ICR (Cohen, 1960). When conducting thematic coding in a group on subject matter capturing latent meaning, an overall kappa between .60 and .80 is most utilitarian. This is because perfect agreement (k = 1.0) may indicate that the codes are too narrow to capture latent content and may suggest “groupthink” or false consensus (conversely, k = 1.0 would be very useful for content coding whereby explicit words are coded and there is little individual coder interpretation). On the other end of the spectrum, too low kappa agreement may indicate poor coding structure, coder misunderstanding or poor validity of codebook definitions. Given this, the current study aimed for an overall, and code-level kappa coefficient value of at least k = .60 (McHugh, 2012). While a useful statistic for identifying meaningful discrepancies in coding, Cohen’s kappa does not identify the source of discrepancy. Unlike in quantitative approaches, in qualitative approaches a lower kappa may be a meaningful and important discrepancy in the codes. Therefore, all divergences in coding or kappas were hand-reconciled via active discourse, to avoid both false consensus and to prevent the removal of codes whose low kappa may indicate thematically - - 66 meaningful discrepancies. Any transcript that was below a k=.60 was discussed, re-calculated, and if necessary the codebook was changed or the transcript re-coded in order to explore the source of coder disagreement (McPhail et al., 2016). To establish reliability, the team used ongoing ICR assessment to calculate the kappa statistic after the coding of each individual transcript. Although this approach is extremely resource intensive, this method helps improve reliability and consistency in coding due to more opportunities to clarify code definitions, identify non-meaningful differences in coder style, and to remove unnecessary codes (O’Connor & Joffe, 2020; McPhail et al., 2016). As such, Cohen’s kappa measuring intercoder reliability was re-calculated after each round of coding per transcript, per codebook iteration. This iterative process occurred until a final structure for codes was achieved, and no new codes emerged, known as “saturation,” and when codes passed k=.60 (McPhail et al., 2016). After this, all transcripts were re-coded with the final codebook frame. Please see for a Figure 1 for a visual depiction of this iterative process. Figure 1 The procedure employed for coding, re-coding and establishing codebook frame. - - 67 To further establish reliability, intracoder reliability was assessed; this measures the consistency of how the same person codes one segment of data, as compared to herself (O’Connor & Joffe, 2020). Assessing intracoder reliability allows for the testing of consistency and validity at the individual level. This revealed coder misunderstandings of codes or definitions which could cause coders to apply a code to the wrong portion of text, leading to “invalid” sources of coder disagreements, or disagreements due to misunderstanding. After re-training coders using intracoder reliability, the overall kappas for the codebook improved. Establishing Validity. Throughout the iterative process of coding, several methods were employed to meaningfully improve the team’s ICR by identifying and mitigating the causes of non-meaningful or invalid kappa scores. ICR may be artificially lowered due to poorly defined units of measurement and coder misunderstanding of codebook definitions. These phenomena can lead to errors in the NVIVO software’s calculation of kappa values and can lead to inconsistent inaccuracies in coder application of the codebook. Therefore, addressing these threats helps to establish the trustworthiness of the kappa such that discrepancies are true coder differences rather than differences in approach, misunderstanding or units of measurement issues. Units of analysis by size were established in the Coding Rulebook. The team identified units of text by length (i.e., one sentence) as the units of measurement for coding, rather than units of text by meaning (McPhail et al., 2016). The team chose to code sentences as the smallest unit of measurement rather than paragraphs, which can lead to less nuanced codes. Precision for the start and end points of coded segments and what to include or not include (i.e., punctuation, transcript timestamps) were also established in the coding rules. Together, these measures ensured that the data NVIVO used to calculate the kappa was consistently and uniformly collected and - - 68 organized for accurate analysis. This reduced the rate of coder disagreement and artificially low kappas, due to issues involving units of measurement. A final threat to coding validity are discrepancies in coding style; in fact, coders who use a “lumping” (or chunking) method or a “splitting” method in their approach to coding can be a main reason for low ICR scores (MacPhail et al., 2015). Lumpers capture too many sentences in their coded segments and splitters tend to code too few lines in their coded segments. As such, lumpers have more overlap in codes with other coders leading to artificially higher kappas in NVIVO, and conversely, splitters may code portions of the “correct” segment but artificially lower the ICR scores because the software does not recognize it as a “match” for other, longer, coded segments. The current team addressed this threat to validity by re-evaluating coding rules for units of measurement in the team’s Coding Rulebook and re-training coders as necessary. Thematic Analysis Once transcripts were completely coded, the head coder and present researcher individually conducted the thematic analysis. The present study utilized a form of Thematic Analysis (TA) developed by Braun & Clarke termed Reflexive Thematic analysis (RT) as the method for analyzing the data (Braun, Clarke & Hayfield et al., 2016). TA enables qualitative researchers to identify patterns of meaning across coded data and provides a thorough approach to qualitative analysis which includes: identifying patterns in the coded data, discovering themes which emerge from these patterns and applying multiple theoretical frameworks to interpret the data and address the research questions (Braun & Clarke, 2019; Braun & Clarke, 2021). This is a form of thematic analysis that is inductive, in that the coding and subsequent theme identification arise from the data. The researcher applied a combination of a priori and inductive analysis while using this approach (MacQueen et al., 1998). - - 69 Theoretical Lenses. RT allows for a flexible approach in the application of theory, in that the researcher can apply multiple frameworks to answer different types of research questions from the same data (Braun & Clarke, 2021). As such, thematic data was interpreted using both a Phenomenological and Critical Feminist lens, rooted in an intersectional framework (O'Connor & Joffe, 2020; Reeves et al., 2008; McHugh, 2014). A Phenomenological process entails focusing on the meanings that emerge from the interviews and clustering them together to understand the meanings that the participants themselves apply to their own statements (Reeves et al., 2008). This was combined with Critical Feminist theory which enables the researcher to filter the data through a critical lens to understand how power dynamics, inequities and social systems (such as gender inequities, provider-patient relationships and patriarchal systems) may be operating through the phenomena described in the data (Reeves et al., 2008; McHugh, 2014). Method of Analysis. To conduct the analysis of coded data, first the researcher identified patterns that arose from the coded data, utilizing Phenomenological and Critical Feminist lenses. These patterns gave way to multiple possible themes which were then compared to extant theory and data on mistreatment to identify areas of divergence and intersection. To identify the salience of these emergent themes, frequency data (i.e., the number of times codes within each theme was used) and descriptive statistics (i.e., the average kappa ICR for a given coded segment within each theme) from the coding process were analyzed. The researcher also conducted word-frequency analyses using NVIVO software to further identify thematic importance of the emerging patterns and to elucidate whether themes that arose were common or outliers within the sub-sample. Through this process, themes were combined, eliminated or further defined until only those themes remained which were adequately supported by the coded data via ICR and frequency data, truly illustrated the Phenomenological voice, were explanatory through a Critical Feminist lens, - - 70 and addressed the research questions. The final themes that emerged through this process were defined and several quotes were chosen to illustrate each theme which either helped to define them, represented the collective experience, or which helped to illustrate diversity of extremes in experiences. Study Aim 6. Triangulation of Mixed-Methods Data Finally, the study triangulated the mixed-methods data to integrate findings into a single, useful, body of results from both the qualitative and quantitative findings. This process provides a comprehensive picture of the findings through the synthesis of both statistical and thematic outcomes (Graham, 2014). To do this, the qualitative themes rendered through RT (Aim 5) were integrated with, compared to, and used to illustrate the final cross-sectional statistical model elucidated by the path analysis (Aim 4). The study triangulated significant statistical findings with thematic findings. Qualitative themes that do not reflect statistical findings were comparatively explored and theoretically discussed, as with other non-significant findings. The approach utilized a method described by Palinkas et al. whereby qualitative and quantitative data were simultaneously collected, analyzed separately, then the qualitative data was “transformed” into a quantifiable medium whereby the strength of the qualitative themes could be interpreted via the strength of statistical correlation and regression relationships shown in the path analysis model (Palinkas, 2011). Together, this final outcome is demonstrated through a mixed methods, mixed-results, visual diagram. This unique triangulation approach combines the statistical power of path analysis with the qualitative analysis method of Reflexive Thematic (RT) analysis and was informed by the visual depictions of systems’-thinking Causal Loop Diagrams (CLD) (Braun, Clarke & Hayfield et al., 2019; Haraldsson, 2004). - - 71 CHAPTER V: RESULTS Quantitative Methods Results Recruitment Participants were recruited over an 8-month period using a combination of online and in-person recruitment. A large proportion of respondents were identified to be fraudulent respondents, many of which were robots taking the study due to the incentive for payment. This is a phenomenon now widely recognized in the field of science research since psychology research went online as a result of the COVID-19 pandemic, with one study finding that as many as 96% of online respondents may be classified as fraudulent or “bots” (Pozzar et al., 2020). As a result, the present research team utilized a fraudulent-response detection system in order to eliminate potential robot and fraudulent respondents in order to increase the validity of the sample. As a result of this method, it was identified that of the total 3,842 survey responses received between August 2022 and March 2023, only 2.86% of those met validity criteria for inclusion in the study (n = 109). 36 respondents were found to be valid responses but were not included in the analysis due to non-responding (filled out <20% of the survey), reported they gave birth at home not at a hospital, or did not give birth in New York City. As such, 109 respondents met criteria for inclusion based on these reliability and validity measures. Descriptive Statistics At the conclusion of data collection, 109 participants completed the online survey and were eligible to be included for data analysis (n =109). Further, 8 of these participants also completed the 1-hour qualitative interview (n = 8). - - 72 Demographic Data The average age of the sample was 34.94 years-old (SD = 4.02). The most observed age in the sample was 37 years-old (Mode = 37.00, n=15, 13.51%), and the median age was 35 years-old. On average, the sample participants were 4.79 months postpartum, with the most participants being 3.90 months postpartum (SD = 2.34). Participants ranged from two-weeks postpartum through 1-year postpartum (Min = 0.13, Max = 12.00). These data are depicted below in Table 1. Table 1 Descriptive Statistics: Demographic Means Variable (n = 109) M SD Min Max Age (in years) 34.94 4.02 22 43 Birth Rate 1.39 0.54 1.00 3.00 Time Since Index Birth (in months) 4.79 2.34 0.13 12.00 Infant Weight (in pounds) 6.87 1.44 3.12 10.87 With respect to race, 73.87% of the sample identifies as Caucasian (n = 82), followed by 10.81% who identify as Asian (n=12), 6.3% who identify as Black (n=7) and 2.70% who identify as Latinx (n=3). Frequencies and percentages of race within this sample may be seen in Table 2. The most frequently observed category of religion within the sample was Reform Judaism (n = 17, 15.32%), followed by Catholic (n=14, 12.61%), and Agnostic (n=13, 11.71%). For the full list of religions represented in the sample and their frequencies and percentages, please see Table 3. - - 73 Table 2 Frequency Table for Race Variable n % Race Asian 12 10.81 Caucasian 82 73.87 Latinx 3 2.70 Mixed 1 0.90 Middle Eastern 1 0.90 Black 7 6.31 Caucasian & Asian 1 0.90 Hispanic 1 0.90 Missing 2 1.80 Note. Due to rounding errors, percentages may not equal 100%. Table 3 Frequency Table for Religion Variable n % Religion Catholic 14 12.61 Jewish (Reform) 17 15.32 Hindu 1 0.90 Buddhist 5 4.50 Atheist 12 10.81 Christian 11 9.91 Agnostic 13 11.71 Jewish (Nondenominational) 4 3.60 Spiritual 9 8.11 Jewish (Conservative) 3 2.70 Jewish (Orthodox) 2 1.80 Jewish 1 0.90 Jehovah’s Witness 1 0.90 Prefers not to answer 18 6.00 Note. Due to rounding errors, percentages may not equal 100%. Most participants in the sample are United States citizens (n=96, 86.49%), 71% reside in the New York City borough of Brooklyn (n = 79, 71.17%), and most of the sample reported a - - 74 household income greater than $150,000 (n = 71, 63.96%). 96.40% of the sample are partnered or have a spouse (n = 107, 96.40%) and 83.78% of the sample are currently employed (n = 93, 83.78%). Most of the sample completed K-12 education (n = 108, 97.30%) as well as some form of higher education (n = 104, 93.69%). For the full list of boroughs and income levels represented in the sample and their frequencies and percentages, please see Table 4. Table 4 Descriptive Statistics: Demographic Frequencies Variable n % Borough of Residence Manhattan 19 17.12 Brooklyn 79 71.17 Queens 2 1.80 The Bronx 4 3.60 Staten Island 2 1.80 Income in Dollars 35,000-90,000 14 12.61 > 150,000 71 63.96 90,000-150,000 22 19.82 Prefers not to answer 4 3.6 Education K-12 108 97.30 Higher Education 104 93.69 Employed Yes 94 85.46 No 14 12.73 Social Services Yes 7 6.36 No 101 91.82 Partner or Spouse Yes 107 97.27 Missing/Prefers not to answer 2 1.82 Citizen of USA Yes 96 87.27 No 12 10.91 - - 75 Mental Health Demographics Mental health demographics are depicted below, in Table 5. With respect to mental health history, 48.65% of the sample endorsed some a history of mental illness (n = 54), including anxiety (n = 21, 18.92%), depression (n=4, 3.60%), and both depression and anxiety (n=9, 8.11%). A majority of the sample have received therapy at some time in their lives (n = 74, 66.67%), and 29.73% of the sample have taken psychiatric medication at some time in their lives (n=33). Finally, 18.02% of the sample (n=20) endorsed history of sexual violence and 5.41% of the sample endorsed a history of intimate partner violence (IPV) (n=6). Table 5 Descriptive Statistics: Mental Health Frequencies Variable n % History of Diagnosed Mental Illness 54 48.65 Anxiety 19 18.92 Depression 4 3.60 Depression & Anxiety 9 8.11 OCD 1 0.91 ADD 1 0.91 PMDD 1 0.91 Lifetime History of Therapy Yes 74 66.67 No 33 29.73 History of Psychiatric Medication 33 29.73 SSRI, SNRI, SARI 18 16.36 Benzodiazepine 7 6.37 History of Sexual Violence Yes 20 18.02 No 87 78.38 History of Intimate Partner Violence Yes 6 5.41 No 100 90.09 - - 76 Birth Demographics With respect to childbirth demographic and outcomes, for 62.16% of the sample, this was their first-time giving birth (primiparous) (n = 69), with the sample reporting an average birth rate of 1.39 per person (SD = 0.54). Nearly all of the sample received prenatal medical care during their most recent pregnancy (n = 107, 96.40%). Most of the sample gave birth at hospitals in the New York City borough of Manhattan (n = 83, 74.77%), followed by 18.02% of respondents who gave birth in Brooklyn (n=20), 2.70% gave birth at a hospital in Queens (n=3), one respondent gave birth in Staten Island and one in the Bronx. Most of the sample had some form of health insurance (n = 106, 95.50%); 85.59% have private health insurance while a small percentage had Medicaid (n=5, 4.50%). 54.95% of respondents reported that they took a childbirth class in preparation for their most recent birth (n = 61), while 77.48% of the sample reported ever taking a childbirth class (n = 86). Most of the sample had a birth plan for their most recent birth (n = 85, 76.58%), and shared it with their treatment team (n = 76, 68.47%). However, only 69.73% of those who shared their birth plan, reported that it was followed by their treatment team (n = 53). Most participants had a birth companion with them (n = 105, 94.59%); 45.95% had a partner with them (n = 51) and 23.42% had a partner and a doula with them (n = 26). Descriptive statistics of the birth demographics may be found in Table 6. - - 77 Table 6 Descriptive Statistics: Birth and Health Variable Frequencies Maternal and Infant Health With respect to medical complications during pregnancy, a minority of participants answered “yes” when asked if they were diagnosed by their provider as “overweight,” (n=13, 11.71%), diabetic (n=8, 7.20%) or hypertensive (n=14, 12.61%) during their most recent pregnancy. 3 participants reported being COVID-19 positive during their childbirth (n = 3, 2.7%). With regards to delivery method, 36.04% of the sample gave birth via Cesarean section (n = 40), 42.50% of which were planned C-sections (n = 17) and 40% of which were considered emergency C-sections (n = 16). 10 respondents experienced hemorrhage during or after their Variable n % Primiparous 69 62.16 Prenatal Care 107 96.40 Childbirth Class, Index Pregnancy 61 54.95 Childbirth Class, Lifetime 86 77.47 Birth Plan 85 76.58 Shared Birth Plan with Providers 76 68.47 Birth Plan Followed by Providers 53 47.74 Birth Companion Present 105 94.59 Partner 51 45.94 Partner & Doula 26 23.42 Friend 2 1.82 Health Insurance 106 95.50 Private Insurance 95 85.59 Medicaid 5 4.50 Hospital Birth Borough Manhattan 83 74.77 Brooklyn 20 18.02 Queens 3 2.70 The Bronx 1 .90 Staten Island 1 .90 - - 78 childbirth (n = 10, 9.01%). 45.95% of the sample reported receiving an epidural (n = 51), and 25.23% of the sample received some form of unwanted medical intervention during their birth (n = 28). 51.35% of the sample were induced for labor (n = 57). Finally, with regards to infant health outcomes, the sample had an average infant weight of 6.87 lbs., or 110.05 ounces (SD = 22.99 ounces). 18.02% of respondents had an infant who spent time in the neonatal intensive care unit (NICU) (n = 20), half of whom spent more than one week in the NICU (n = 9, 8.11%). Health outcomes data may be found in Table 7. Table 7 Descriptive Statistics: Gestational and Infant Health Frequencies Variable n % Perinatal Health Variables BMI >30 13 11.71 Gestational Diabetes 8 7.20 Hypertension 14 12.61 COVID-19 Positive at Birth 3 2.7 Cesarean Section 40 36.04 Planned Cesarean Section 17 15.32 Emergency Cesarean Section 16 14.14 Hemorrhage 10 9.01 Medical Interventions in Labor Epidural 51 45.95 Induced 57 51.35 Episiotomy 7 6.31 Unwanted Intervention 28 25.23 Infant Health Outcomes NICU 20 18.02 >1 Week on NICU 9 8.11 - - 79 Prevalence, Frequency and Forms of Mistreatment and Mental Health Next, the analysis measured and statistically described the observed frequencies and forms of mistreatment in maternal health settings, and the observed frequencies and forms of mental illness among the sample. To conduct the analysis, the researcher utilized Intellectus, a statistical analysis software package (Intellectus Statistics, 2023). Please see Appendix D for full Spearman correlation tables for variables of interest. Mistreatment in Maternity Results To establish the prevalence of mistreatment, the study measured the average score on the index measures of Mothers on Respect index (MORi) Mother’s Autonomy in Decision Making scale (MADM), shown in Table 8, the frequency of respondents were above cut-off on the index measures, as well as item-level prevalence per measure. Table 8 Summary Statistics Table for MADM and MORi Scores Variable M SD Min Max MADM_Total 30.55 7.95 8.00 42.00 MORI_Total 67.28 12.34 27.00 84.00 The average total score on the MADM was 30.55 (SD = 7.95). On the MADM, the possible range of scores is 7-42; scores <7-15 indicate Very Low Patient Autonomy, scores 16-24 indicate Low Patient Autonomy, 25-33 indicate Moderate Patient Autonomy, and scores of 34-42 indicate High Patient Autonomy. Higher scores are better, and indicate the patient had greater control over decision making and was “in the lead” in her own care (Vedam et al., 2017). This mean score indicates that, on average, the current sample experienced Moderate patient autonomy in maternity care and labor. However, 15.79% of the sample (n=17) indicated that they experienced Low to - - 80 Very Low patient autonomy. Summary statistics may be seen in Table 5, and a histogram depicting the spread of the scores may be seen in Figure 2. Please see Appendix E for the MADM item frequency table. Figure 2 Histogram of total MADM scores where 42 is the maximum highest score. The observations for MORi had an average score of 67.28 (SD = 12.34). On the MORi, higher scores indicate higher levels of respect, such that scores <14-31 indicate Very Low Respect, 32-49 indicate Low Respect, 50-66 indicate Moderate Respect, and >67-84 indicate High Respect (Vedam et al., 2017). This mean score indicates that on average, this sample experienced Moderate to High levels of respect in maternity care, with 10.47% endorsing low to very low respect (n=11). The summary statistics can be found in Table 6. Please see Appendix F for MORi Parts A, B and C frequency tables. - - 81 Figure 3 Histogram of total MORI scores where 84 is the maximum highest score. Part A of the MORi. This section of the questionnaire asked participants to reflect on their comfort level regarding making decisions about their own and their infant’s medical care. The sample’s response pattern indicates that overall, the respondents felt comfortable asking questions of their healthcare providers. However, 31.54% of the sample endorsed feeling pressured or pushed into accepting recommendations from the treatment team. And, a minority of the sample felt uncomfortable asking questions of their treatment teams (n = 11). Part B of the MORi. Respondents were asked to reflect on how their race, sexual orientation, health insurance status, and personal opinions impacted the treatment they received from their care providers. The vast majority of the sample did not feel they were treated poorly due to these factors. 5.4% of the sample felt they were treated poorly by their providers due to their race (n = 6) and 1% reported being treated poorly due to gender identity or sexual orientation. 2.7% of respondents felt they were treated differently due to their insurance status. Finally, 13.63% - - 82 of respondents reported that they felt they were treated poorly due to a difference of opinion from their care providers regarding the treatment of themselves or their infant (n = 15). Part C of MORi. Finally, the questionnaire asked respondents to rate to what degree they feel they held back from asking questions of their providers. 33.33% of respondents reported they held back from asking questions or discussing their concerns because the care provider seemed rushed (n = 45), while 12.62% of the sample reported they did not discuss their concerns or questions because what they wanted differed from the recommendations of their care provider. Finally, 36.94% of participants reported that they held back from asking questions because they thought their care provider would think they were “being difficult” (n = 41). Mental Health and Psychosocial Results Next, the study assessed the prevalence of mental illness and trauma history in the cohort by measuring the percent of participants who were at or above cut-off score for the psychological measures PHQ-9, PCL-5 and ACE. The study also assessed the prevalence of psychosocial phenomenon including perceived racism and mother-infant bonding quality. The results are presented in Table 9. Table 9 Summary Statistics Table for Mental Health Variables of Interest Variable M SD Min Max PHQ_Total 4.76 4.01 0.00 20.00 PCL5_Total 10.68 12.99 0.00 70.00 ACE_Total 1.50 1.91 0.00 10.00 PR_Total 53.68 7.94 23.00 70.00 MIBS_Total 3.25 3.13 0.00 15.00 - - 83 Patient Health Questionnaire, 9-item. The average score on the PHQ-9 was 4.76 (SD = 4.01). The cut-off score for depression on the PHQ-9 is 8-11, with a score of 1-4 indicating Minimal Depression, 5-9 indicating Mild Depression, 10-14 indicating Moderate Depression, 15-19 Moderately Severe Depression, and 20-27 indicating Severe Depression (Kroenke, Spitzer & Williams, 2001; Pfizer, 1999; Manea, Gilbody & McMillan, 2012). Therefore, the present sample ranges from no depressive symptoms to severe depressive symptoms, with 21% of the sample (n=22) at or above the cutoff for depression. The data are symmetrical and normally distributed, indicating there are minimal outliers and skewness (Westfall & Henning, 2013). PTSD Checklist for DSM-5. The average PCL-5 for the current sample was 10.68 (SD = 12.99). A cutoff score above 30 on this measure indicates probable PTSD (calculation of the DSM-5 cluster criteria is required for full clinical diagnosis). The majority of participants in this sample do not endorse PTSD symptoms. The study found a 6% rate of likely PTSD in the sample (n=8) (Bovin et al., 2016; Mayopoulos et al., 2021). Given that the present study is cross-sectional in design, it is not possible to establish whether the PTSD was present prior to the index birth. Finally, the data are skewed, with a large proportion of the sample indicating no to low PTSD symptoms (Skewness = 2.20), and the distribution is skewed positive due to outliers (Kurtosis = 5.85) (Westfall & Henning, 2013). Adverse Childhood Experiences Questionnaire (ACE). The average score on the ACE questionnaire was 1.50 (SD = 1.91). The ACE has ten questions which ask the patient to determine if, before age 18, they experienced instances of emotional abuse, physical abuse, molestation, a primary caregiver with mental illness, a primary caregiver who was incarcerated, and other adverse experiences. A score of 0 on the ACE means there was no exposure to any of the listed adverse childhood experiences, whereas a score of 10 means that the person had complete exposure. The - - 84 higher the score, the more likely the person is at risk for poorer health outcomes, with scores of 6 or higher significantly increasing health risks and reducing lifespan (Felitti et al., 1998; acestudy.org). The present sample has, on average, endorsed low exposure to adverse childhood experiences, with ~3 % of the sample (n=3) endorsing 6 or more significant adverse childhood events. The data are symmetrical about the mean and do not follow a normal distribution, due to outliers in the right tail of the distribution (Westfall & Henning, 2013). Perceived Racism. The observations for PR had an average score of 53.68 (SD = 7.94). The maximum score on the PR is 80, with higher scores indicating higher levels of perceived discrimination from care providers, disrespect from care providers due to race, and perceptions of unfair treatment due to race, ethnicity or cultural heritage (Vedam, Stoll & Taiwo et al., 2019). This sample perceives a moderate to high level of discrimination from care providers for, themselves and others. The data are symmetrical about the mean with a normal distribution (Westfall & Henning, 2013). The PR assessed perceived racism within maternal healthcare settings across several domains. 72% of the sample felt that doctors and nurses do not act the same towards pregnant women of all races and ethnicities (n = 81). 41.44% of respondents felt they are affected by discrimination (n = 46). and 22.53% reported that racism is a problem in their life (n = 25). In relation to their perceptions of racism in medical care overall, 44.14% of respondents agreed that racial discrimination is common in doctors’ offices and roughly one third of respondents felt that women of different races do not receive the same kind of care in hospitals (n = 43, 38.74%). Maternal Infant Bonding Questionnaire (MIBS). The average score on the Maternal Infant Bonding Questionnaire (MIBS) was 3.25 (SD = 3.134). The MIBS assesses maternal-infant bonding by asking the mother to rate the degree to which she feels different emotions towards her - - 85 infant (Taylor et al., 2005). There is a possible score of 0 to 24, with a higher score indicating more impaired maternal bonding. A mean of 3.25 indicates that, on average, the present sample does not indicate a trend of impaired mother-infant bonding (Taylor et al., 2005; Van Bussel, Spitz & Demyttenaere, 2010). These data are normally distributed and considered to be symmetrical about the mean with no outliers (Westfall & Henning, 2013). Risk and Protective Factors of Mistreatment The study explored for whom mistreatment is occurring and identified what (if any) effects arise from women’s mistreatment in labor to identify potential “risk” and “protective” factors of mistreatment in maternal care. To do this, the study determined what patient-level, categorical variables may act as moderators of mistreatment in labor, as defined by scores on the MORi and MADM. Preparatory Analyses The regression Ordinary Least Squares (OLS) assumptions of linearity, normality, independence, absence of multicollinearity and heterogeneity of variance were assessed and met prior to running the regression analyses (Berry, 1993). The linear relationships of the variables were assessed using scatter plots and R squared values, and a correlation matrix was calculated to examine multicollinearity between the dependent variables. The assumption of normal distribution of errors was assessed by plotting the quantiles of the model residuals against the quantiles of a Chi-square distribution, also called a Q-Q scatterplot (DeCarlo, 1997). Homoscedasticity was evaluated by plotting the residuals against the predicted values (Bates et al., 2014; Field, 2017; Osborne & Walters, 2002). To identify outliers in the data, studentized residuals were calculated, and the absolute values were plotted against the observation numbers (Field, 2017; Pituch & Stevens, 2015). Mahalanobis distances were also calculated and - - 86 compared to a χ2 distribution to assess residuals in certain models run in the present study (Newton & Rudestam, 2012). Heterogeneity of variance was assessed through p-p plots and fitted value v. residual plots and Box's M tests. Independence was assessed using VIF calculations. Skewness and kurtosis were assessed prior to running the analyses. Risk and Protective Factors of Mistreatment A multivariate analysis of variance (MANOVA) was conducted to assess if there were significant differences in the linear combination of the total scores for the two mistreatment measures, MADM and MORi, between the levels of the following variables: (1) Race, (2) Private Health Insurance, (3) Medicaid status, (4) Birth Plan, (5) Emergency C-section, (6) Unwanted Interventions, (7) Primiparous, (8) Birth Companion, (9) Gestational hypertension, (10) Overweight, and, (11) Gestational diabetes. The MANOVA found that the main effect for Unwanted Interventions was significant, F(2, 25) = 8.83, p = .001, η2p = 0.41, suggesting the linear combination of MADM total and MORi was significantly different between those who did and did not have unwanted interventions during their childbirth. No other main effects were significant. The MANOVA results are presented in Table 10, on the following page. - - 87 Table 10 MANOVA Results for MADM Total and MORI Total (by Race, Health Insurance, Birth Plan, C- section Emergency, Unwanted Interventions, Parity, and Birth Companion) Variable Pillai F df Residual df p ηp2 Race 0.39 2.08 6 52 .072 0.19 Private_Health_Insurance 0.10 0.66 4 52 .624 0.05 Medicaid 0.14 1.97 2 25 .161 0.14 Birth_Plan 0.02 0.21 2 25 .809 0.02 C_section_Emergency 0.15 2.21 2 25 .131 0.15 Unwanted_Interventions 0.41 8.83 2 25 *.001 0.41 Overweight 0.01 0.39 2 100 .675 0.01 Diabetes 0.01 0.54 2 100 .586 0.01 Primaparous 0.15 2.24 2 25 .128 0.15 Hypertension 0.02 0.96 2 100 .387 0.02 Birth_Companion 0.07 0.95 2 25 .401 0.07 To further examine the effects of these variables on MADM and MORi totals, an analysis of variance (ANOVA) was conducted for each independent variable. An ANOVA was conducted to determine whether there were significant differences in MADM and MORi total by (1) Race, (2) Private Health Insurance, (3) Medicaid, (4) Birth Plan, (5) Emergency C-section, (6) Unwanted Interventions, (7) Primiparous, and (8) Birth Companion. The ANOVA was examined based on an alpha value of .05. The results of the ANOVA for MADM were not significant, F(11, 27) = 2.08, p = .060, indicating the differences in MADM total among the levels of Race, Private Health Insurance, Medicaid, Birth Plan, Emergency C-Section Unwanted Interventions, Primiparous, and Birth Companion were all similar. However, the main effect for Unwanted Interventions was significant, F(1, 27) = 9.52, p = .005, ηp2 = 0.26, indicating there were significant differences in total MADM scores between those who did and did not receive an unwanted intervention during their childbirth. Please see Figure 4, below, for a visual depiction of this main effect. No other main effects that - - 88 were explored in relation to MADM were significant, as shown in the ANOVA results table, below, in Table 11. Figure 4 Means of MADM_Total by Unwanted_Interventions with 95.00% CI Error Bars Table 11 Analysis of Variance Table for MADM Total (by Race, Health Insurance, Birth Plan, C- section Emergency, Unwanted Interventions, Parity, and Birth Companion) Term SS df F p ηp2 Race 77.58 3 0.48 .699 0.05 Private_Health_Insurance 83.06 2 0.77 .472 0.05 Medicaid 154.22 1 2.86 .102 0.10 Birth_Plan 8.36 1 0.16 .697 0.01 C_section_Emergency 144.22 1 2.68 .113 0.09 Unwanted_Interventions 512.97 1 9.52 *.005 0.26 Primaparous 36.17 1 0.67 .420 0.02 Birth_Companion 106.42 1 1.98 .171 0.07 Residuals 1,454.27 27 - - 89 The results of the ANOVA for MORi were significant, F(11, 26) = 3.97, p = .002, indicating there were significant differences in the total MORi scores among the levels of Race, Private Health Insurance, Medicaid, Birth Plan, Emergency C-section, Unwanted Interventions, Primiparous, and Birth Companion. The main effect for Race and MORi was significant, F(3, 26) = 4.42, p = .012, ηp2 = 0.34, indicating that amount of respect women received in maternity care significantly varied by race (Figure 5.). Further, the main effect for Unwanted Interventions and MORi was also significant, F(1, 26) = 14.80, p < .001, ηp2 = 0.36, indicating there were significant differences the amount of respect in maternity care between those who did and did not receive unwanted interventions in labor (Figure 6.). Finally, the main effect of Primiparous was also significant, F(1, 26) = 4.62, p = .041, ηp2 = 0.15, indicating there were significant differences in total MORi scores between those who had previously given birth and those who had not (Figure 7.). No other main effects were significant. The ANOVA results are presented in Table 12. Figure 5 Figure 6 Means of MORI_Total by Race with Means of MORI_Total by 95.00% CI Unwanted_Interventions with 95.00% CI - - 90 Figure 7 Means of MORI_Total by Primaparous with 95.00% CI Error Bars Table 12 Analysis of Variance Table for MORI Total (by Race, Health Insurance, Birth Plan, C- section Emergency, Unwanted Interventions, Parity, and Birth Companion) Term SS df F p ηp2 Race 1,276.73 3 4.42 *.012 0.34 Private_Health_Insurance 50.67 2 0.26 .771 0.02 Medicaid 248.40 1 2.58 .120 0.09 Birth_Plan 40.55 1 0.42 .522 0.02 C_section_Emergency 342.94 1 3.56 .070 0.12 Unwanted_Interventions 1,426.10 1 14.80 *< .001 0.36 Primaparous 444.69 1 4.62 *.041 0.15 Birth_Companion 50.11 1 0.52 .477 0.02 Residuals 2,504.87 26 Moderators of Respect in Maternity Care (MORi) To examine the presence of moderator variables on the relationship between predictor variables and total MORi scores, the study conducted several multiple linear regressions and examined the interaction terms using moderation analysis, with the following variables: (1) Race, - - 91 (2) “High-risk” pregnancy factors including obesity, diabetes and hypertension, (3) Insurance Status, (4) Perceived Racism, (5) Birth Plan, (3) Unwanted Intervention, (4) Childbirth Education, and (5) Emergency C-Section. Race, High-Risk Pregnancy and Insurance. To examine the potential presence of moderator variables on the significant relationship between racial identity on MORi, the study conducted several multiple linear regression analyses and examined the interaction terms. The overall model was not significant for Perceived Racism (p = .072), Race and Diabetes (p = .106), Race and Hypertension (p = .091), or Race and Overweight (p = .165). A moderation effect between Race and Insurance Status on MORi could not be conducted because these variables did not meet OLS assumptions due to perfect multicollinearity between Caucasian “Yes” and Private Insurance “Yes,” meaning all White-identifying participants had private health insurance. Please see Appendix I for Tables 13, 14, 15 and 16 for the moderation analysis tables. These results indicate that these predictor variables did not account for a substantial proportion of variance in MORi and suggest there is no presence of a moderation within the relationship between race of the patient and respect in maternity care. As such, the relationship between race and respect is not moderated by one’s perception of racism, one’s physical health complications, or insurance status meaning that the relationship between being a woman of color and experiencing less respect in maternity care is not moderated by medical complications, having “better” or “worse” insurance or even how one perceives race to affect their own care. Emergency C-Section and Childbirth Class. A multiple linear regression analysis was conducted to determine if Childbirth Class and Emergency C-section had a moderating effect on MORi. The overall model was significant, R2 = 0.21, F(3, 34) = 3.04, p = .042, indicating the predictors accounted for 21.17% of variance in the total MORi score. Since the overall model was - - 92 significant, moderation was assessed by examining the interaction between Emergency C-section and Childbirth Class. This interaction was not significant, (p = .565). The main effect for Emergency C-section was not significant (p = .937), however the main effect for the “no” category of Childbirth Class was significant, B = 12.34, t(34) = 2.69, p = .011, indicating that having not ever taken a childbirth class tends to change the value of MORi by 12.34 points, on average, compared to those who have taken one, when holding for Emergency C-section variable. Please see Appendix I, Table 17, for moderation analysis table. Emergency C-Section and Birth Plan. A moderation between Emergency C-section and Birth Plan variable on MADM and MORi could not be conducted due to perfect multicollinearity between these variables. Every participant who had an emergency C-section in the present sample, did not have a birth plan. As such, these variables failed to meet the OLS assumptions to conduct a regression. Unwanted Intervention and Birth Plan. A multiple linear regression analysis was conducted to determine if Birth Plan and Unwanted Interventions had a moderating effect on MORi total score. The overall model was significant, R2 = 0.22, F(3, 101) = 9.33, p < .001, indicating the predictors accounted for 21.71% of variance in total MORi score. Next, a moderation was assessed by examining the interaction between Unwanted Intervention and Birth Plan on MORi. This interaction was not significant (p = .241). The main effect for Unwanted Intervention was significant, B = -12.46, t(101) = -4.73, p < .001, indicating that a one-unit increase in Unwanted Intervention will result in a 12.46 decrease in the total MORi score, on average, among those who did have a birth plan. The main effect for the “No” category of Birth Plan was not significant ( p = .085). Please see Appendix I, Table 18, for moderation analysis table. - - 93 Unwanted Intervention and Race. A multiple linear regression analysis was conducted to determine if Race and Unwanted Intervention had a moderating effect on MORi total. The overall model was significant, R2 = 0.26, F(3, 101) = 12.08, p < .001. The moderation analysis was not significant (p = .189). The main effect for Unwanted Intervention was significant, B = -13.00, t(101) = -5.43, p < .001, indicating that a one-unit increase in Unwanted Intervention will result in a 13.00 point decrease in MORi scores, on average, holding for Race. The main effect for Race was also significant, B = 7.32, t(101) = 2.98, p = .004, indicating that a one-unit increase in Race (towards Caucasian) will result in a 7.32 change in MORi on average, holding for unwanted interventions. The moderation analysis table is in Appendix I, Table 19. Unwanted Intervention and Childbirth Class. Finally, a multiple linear regression analysis was conducted to determine if Childbirth Class and Unwanted Intervention had a moderating effect on MORi. The overall model was significant, R2 = 0.22, F(3, 100) = 9.18, p < .001, but the interaction was not significant (p = .758), indicating a lack of evidence that not taking a childbirth class moderates the relationship between having an unwanted intervention and respect in maternity care. The main effect for Unwanted Intervention was significant, B = -12.11, t(100) = -4.54, p < .001, indicating that a one-unit increase in Unwanted Intervention will result in a 12.11 point decrease in MORi, on average, among those who took a childbirth class, meaning more unwanted interventions are associated with less respect in maternity care. The main effect for the “No” category of Childbirth Class was not significant (p = .104), when holding for the Unwanted Intervention variable. Please see Appendix I, Table 20 for the moderation analysis table. Moderators of Autonomy in Decision-Making in Maternity Care (MADM) To examine the presence of moderator variables in the relationship between predictor variables and total MADM scores, the study conducted several multiple linear regressions and - - 94 examined the interaction terms using moderation analysis with the following variables: (1) Unwanted Interventions, (2) Birth Plan, (3) Race, (4) Childbirth Education, and (5) Emergency C-section. Unwanted Interventions and Birth Plan. A multiple linear regression analysis was conducted to determine if Birth Plan and Unwanted Interventions had a moderating effect on total MADM scores. The overall model was significant, R2 = 0.22, F(3, 103) = 9.60, p < .001, indicating the predictors accounted for 21.86% of variance in MADM. Moderation was assessed by examining the interaction between Unwanted Interventions and Birth Plan. The interaction between Unwanted Interventions and the “no” category of Birth Plan was significant, B = 11.29, t(103) = 2.02, p = .046, indicating that having no birth plan strengthens the effect of Unwanted Interventions on the MADM total, as compared to those who indicated they did have a birth plan. A moderation plot was generated by plotting the regression lines for each category of Birth Plan. The moderation plot is presented in Figure 8. Figure 8 Regression lines for MADM_Total predicted by Unwanted_Interventions for each category of Birth_Plan - - 95 The main effect for Unwanted Interventions was significant, B = -8.51, t(103) = -5.01, p < .001, indicating that a one-unit increase in Unwanted Interventions will result in a -8.51 change in total MADM scores, on average, when respondents responded “Yes” to Birth Plan. In other words, among those with a birth plan, those who experienced unwanted interventions had lower autonomy scores. The main effect for the “No” category of Birth Plan was not significant (p = .097). The moderation analysis table is presented in Table 21, below. Table 21 Moderation Analysis Table with MADM_Total Predicted by Unwanted_Interventions Moderated by Birth_Plan Predictor B SE β t p (Intercept) 30.20 0.79 0.00 38.24 *< .001 Unwanted_Interventions -8.51 1.70 -0.47 -5.01 *< .001 Birth_Plan “No” 3.24 1.94 0.17 1.67 .097 Unwanted_Interventions:Birth_Plan “No” 11.29 5.59 0.21 2.02 *.046 Finally, a simple slopes analysis was conducted to further explore the effect of Birth Plan on the relationship between Unwanted Interventions and MADM total. The coefficient for Unwanted Interventions with Birth Plan fixed to the “No” category was not significant, B = 2.79, p = .602. The coefficient for Unwanted Interventions with Birth Plan fixed to the “Yes” category was significant, B = -8.51, p < .001. This suggests that the effect of unwanted interventions on MADM strengthens when one has a birth plan. The results of the simple slopes analysis are presented in Table 22. - - 96 Table 22 Simple slopes analysis for Birth_Plan moderating the relationship between Unwanted_Interventions and MADM_Total Values of Birth_Plan B SE % CI t p No 2.79 5.33 [-7.78, 13.35] 0.52 .602 Yes -8.51 1.70 [-11.88, -5.14] -5.01 < .001* Unwanted Interventions and Race. Then, a multiple linear regression analysis was conducted to determine if Race and Unwanted Interventions had a moderating effect on MADM. The overall model was significant, R2 = 0.24, F(3, 103) = 10.84, p < .001, indicating that Race and Unwanted Interventions accounted for 23.99% of variance in MADM scores. Since the overall model was significant, moderation was assessed by examining the interaction between Unwanted Intervention and Race, which was significant, B = 9.94, t(103) = 2.65, p = .009. This indicates that for those who identify as Caucasian, the effect of Unwanted Intervention on MADM is stronger than for those who do not identify as Caucasian. The results of the moderation analysis are shown in Table 23. Table 23 Moderation Analysis Table with MADM_Total Predicted by Unwanted_Interventions Moderated by Caucasian Yes/No Predictor B SE β t p (Intercept) 30.40 0.69 0.00 44.24 *< .001 Unwanted_Interventions -8.01 1.56 -0.44 -5.12 *< .001 Caucasian Yes/No 1.67 1.60 0.09 1.04 .299 Unwanted_Interventions:Caucasian Yes/No 9.94 3.75 0.23 2.65 *.009 A moderation plot was generated by dichotomizing Race into High and Low categories using a median split. The High category indicates all observations of Race above the median (all - - 97 Caucasian respondents), and the Low category specifies all observations of Race below the median (non-Caucasian respondents). The moderation plot is presented in Figure 9. Figure 9 Regression lines for MADM_Total predicted by Unwanted_Interventions for the High and Low categories of Caucasian Yes/No Lastly, a simple slopes analysis was conducted to further explore the effect of Race on the relationship between Unwanted intervention and MADM. The regression coefficient for Unwanted Intervention was calculated while holding Race constant at its mean value, one standard deviation above the mean, and one standard deviation below the mean. The coefficient for Unwanted Intervention with Race fixed to a value of 0.00 was significant, B = -15.53, p < .001. The coefficient for Unwanted Intervention with Race fixed to a value of 1.00 was also significant, B = -5.59, p = .002. This suggests that as Race increases, the relationship between Unwanted Intervention and MADM strengthens. The results of the simple slopes analysis are presented in Table 24. - - 98 Table 24 Simple Slopes Analysis for Caucasian Yes/No Moderating the relationship between Unwanted Interventions and MADM total Values of Caucasian Yes/No B SE % CI t p No -15.53 3.31 [-22.09, -8.98] -4.70 < .001* Yes -5.59 1.77 [-9.11, -2.07] -3.15 .002 Unwanted Interventions and Childbirth Class. A multiple linear regression analysis was conducted to determine if Childbirth Class and Unwanted Intervention had a moderating effect on MADM. The overall model was significant, R2 = 0.21, F(3, 102) = 8.78, p < .001, indicating the predictors accounted for 20.53% of variance in the MADM total score. The moderation analysis was not significant (p = .677), indicating a lack of evidence that absence of childbirth education moderates the relationship between unwanted interventions and total MADM scores. And once more, the main effect for Unwanted Intervention on MADM was significant, B = -7.24, t(102) = -4.18, p < .001. The results of the moderation analysis may be found in Appendix I, Table 25. Emergency C-Section and Childbirth Class. Finally, a multiple linear regression analysis was conducted to determine if Childbirth Class and Emergency C-section had a moderating effect on MADM. The overall model was not significant (p = .261). These findings suggest there is insufficient evidence to support the existence of a moderating effect. Please see Appendix I, Table 26 for the results of the moderation analysis. Relationship Between Mistreatment and Mental Health To assess the relationship between mistreatment, as measured by MORi and MADM, and postpartum mental health, as measured by the Patient Health Questionnaire (PHQ-9), the PTSD Checklist for DSM-5 (PCL-5), trauma history, as measured by the Adverse Childhood Experiences Scale (ACE), and current attachment quality, as measured by the Mother Infant Bonding Scale - - 99 (MIBS), several Spearman correlation analyses were conducted. Cohen's standard was used to evaluate the strength of the relationships (Cohen, 1988). The result of the correlations was examined using the Holm correction to adjust for multiple comparisons based on an alpha value of .05. The results of which are presented in Table 27. Table 27 Spearman Correlation Results Among MORI_Total, MADM_Total, and Mental Health Measures Total Scores Combination r 95.00% CI n p MORI_Total-PHQ_Total -.24 [-.41, -.05] 104 *.028 MADM_Total-PHQ_Total .00 [-.19, .20] 104 .967 MADM_Total-PCL5_Total -.14 [-.33, .05] 102 .151 MORI_Total-PCL5_Total -.51 [-.64, -.35] 102 *< .001 MADM_Total-ACE_Total -.25 [-.42, -.06] 102 *.024 MORI_Total-ACE_Total -.21 [-.39, -.02] 102 *.030 MIBS_Total-MADM_Total .07 [-.13, .26] 102 .984 MIBS_Total-MORI_Total -.06 [-.25, .14] 102 .984 Note. p-values adjusted using the Holm correction. With respect to postpartum mental health, a significant negative correlation was observed between MORi and PHQ-9 scores, with a correlation of -.24, indicating a small effect size (p = .028, 95.00% CI = [-.41, -.05]). This suggests that as MORi scores increases, PHQ-9 tends to decrease. In other words, the more respect respondents received in their maternity care, the lower their self-reported postpartum depression symptoms. A significant negative correlation was observed between MORi and PCL-5, with a correlation of -.51, indicating a large effect size (p < .001, 95.00% CI = [-.64, -.35]) (Cohen, 1988). This suggests that higher MORi scores are correlated with lower PCL-5 scores, such that those who experience more respect in maternity care - - 100 endorse less PTSD symptoms. There were no significant correlations between PHQ-9 and PCL-5 with MADM. To assess the correlation between mistreatment and childhood adversity, a Spearman correlation was conducted. A significant negative correlation was observed between MADM and ACE scores, with a correlation of -.25, indicating a small effect size (p = .024, 95.00% CI = [-.42, -.06]) (Cohen, 1988). This suggests that those who endorsed more adverse events in their childhood, tended to report less autonomy and decision-making power in their maternity care. Likewise, a significant negative correlation was observed between ACE and MORi, with a correlation of -.21, indicating a small effect size (p = .030, 95.00% CI = [-.39, -.02]). Finally, with respect to mother-infant bonding, the Spearman correlation found no significant correlations between the Mother Infant Bonding Scale (MIBS) and MADM nor MORi. Moderators of Mental Illness History and Postpartum Mental Health: MADM & MORi To examine how the mistreatment variables may act as moderators between mental health history and postpartum mental health, the study conducted several analyses moderation analyses. PHQ-9. A multiple linear regression analysis was conducted to determine if different levels of autonomy in labor, as measured by MADM, and Mental Illness History (dichotomized into a “yes/no” scale dummy variable) had a moderating effect on PHQ-9. The overall model was not significant, (p = .559), therefore interactions were not assessed. The multiple linear regression examining if MORi and Mental Illness History had a moderating effect on total PHQ-9 scores was significant, R2 = 0.16, F(3, 99) = 6.06, p < .001, indicating the predictors accounted for 15.52% of variance in PHQ-9 scores. However, the interaction was not significant (p = .545) indicating that MORi does not moderate the relationship between Mental Illness History and PHQ-9. The moderation analysis tables may be found in Appendix I, Tables 28 and 29. - - 101 PCL-5. Next, a multiple linear regression analysis was conducted to determine if MADM and Mental Illness History had a moderating effect on PCL-5 total. The overall model was not significant (p = .053) and suggests there is insufficient evidence to support the existence of a moderating effect. The overall model assessing if MORi total and Mental Illness History had a moderating effect on PCL-5 total was significant, R2 = 0.38, F(3, 97) = 20.04, p < .001, however the interaction was not significant (p = .493), indicating a lack of evidence that MORi moderates the relationship between Mental Illness History and total PCL-5 scores. The main effect for MORi was significant, B = -0.62, t(97) = -7.61, p < .001. This negative correlation indicates that a one-unit increase in MORi will result in a -0.62 change in PCL-5 on average, holding for any history of mental illness. This means that as respect in maternity care increases, postpartum PTSD symptoms decrease. The moderation analysis tables are in Appendix I, Tables 30 and 31. Suicidal Symptoms. A multiple linear regression analysis was conducted to determine if MORi total and Mental Illness History had a moderating effect on Suicidal symptoms, as measured by PHQ-9, question #9. The overall model was not significant (p = .482). These results are shown in Table 32, Appendix I. The study was unable to examine the relationship between MADM, suicidal symptoms and Mental Illness history because these variables failed to meet the OLS regression assumptions of homoscedasticity and normality. Moderators of Childbirth Variables and Postpartum Mental Health: MADM and MORi Next, multiple regressions and moderation analyses were run to examine how the mistreatment variables may act as moderators between childbirth variables, including (1) Birth Plan, (2) Emergency C-section, (3) NICU time, with postpartum mental health measures. - - 102 Birth Plan and PHQ-9. A multiple linear regression analysis was conducted to determine if total MADM scores and presence of a birth plan had a moderating effect on total PHQ-9 scores. Birth Plan was turned into a scale-level variable by re-coding it as a “dummy” variable. The overall model was not significant (p = .763), indicating there is insufficient evidence of a moderating effect. The overall model examining if MORi and Birth Plan had a moderating effect on PHQ-9 was significant, R2 = 0.17, F(3, 99) = 6.99, p < .001, however the interaction was not significant (p = .221). See Appendix I, Tables 33 and 34 for these results. Birth Plan and PCL-5. Next, a multiple linear regression analysis was conducted to determine if total MADM and MORi scores and presence of a birth plan had a moderating effect on total PCL-5 scores. A multiple linear regression analysis was conducted to determine if MORi total and Birth Plan had a moderating effect on PCL-5 total. The overall model for MORi was significant, R2 = 0.37, F(3, 97) = 19.39, p < .001, however the interaction was not significant (p = .805). The overall model for MADM was significant, R2 = 0.14, F(7, 93) = 2.12, p = .049, indicating the predictors accounted for 13.74% of variance in the PCL-5 scores. Moderation was assessed by examining the interaction between Birth Plan and the different levels of MADM scores: Very Low, Low, Moderate, and High Patient Autonomy. The main effect for the Very Low Patient Autonomy category of MADM was significant, B = 14.44, t(93) = 2.35, p = .021, indicating that the Very Low Patient Autonomy category on the MADM tends to change the total value of PCL-5 by 14.44 units on average. The results of the moderation are presented in Table 35. The moderation plot is presented in Figure 10. - - 103 Table 35 Moderation Analysis Table with PCL-5 Predicted by Birth_Plan Moderated by MADM_Levels Predictor B SE β t p (Intercept) 10.81 4.18 0.00 2.59 *.011 Birth_Plan 14.00 12.82 0.47 1.09 .278 MADM (Moderate Pt Autonomy) -2.74 4.56 -0.11 -0.60 .549 MADM (High Pt Autonomy) -1.29 4.62 -0.05 -0.28 .780 MADM (Very Low Pt Autonomy) 14.44 6.14 0.31 2.35 *.021 Birth_Plan:MADM (Moderate Pt Autonomy) -11.97 13.54 -0.26 -0.88 .379 Birth_Plan:MADM (High Pt Autonomy) -12.98 13.53 -0.29 -0.96 .340 Birth_Plan:MADM (Very Low Pt Autonomy) -20.14 18.25 -0.16 -1.10 .273 Figure 10 Regression lines for PCL5_Total predicted by Birth_Plan for each category of MADM_Levels Emergency C-Section and PHQ-9. Next, a multiple linear regression analysis was conducted to determine if levels of MORi and MADM scores and Emergency C-Section had a moderating effect on PHQ-9 total. The overall models for both MADM (p = .869) and MORi (p = .164) were not significant, indicating the predictors did not account for a substantial proportion of - - 104 variance in PHQ-9 total scores. These findings suggest there is insufficient evidence of a moderating effect. The results of these analyses are located Tables 36 and 37 in Appendix I. NICU x PHQ-9. To determine if total MADM and MORi scores and time in the NICU had a moderating effect on PHQ-9 total, a multiple linear regression analyses were examined. The overall model for MADM was not significant (p = .359). The overall model for MORi was significant, R2 = 0.15, F(3, 99) = 5.79, p = .001, indicating the predictors accounted for 14.93% of variance in PHQ-9 total scores. The interaction was not significant (p = .857), indicating a lack of evidence that MORi scores moderate the relationship between NICU and PHQ-9 total. The main effect for NICU was not significant, (p = .708), indicating that this variable did not have a substantial effect on total PHQ-9 scores. Please see moderation Tables 38 and 39 in Appendix I. NICU x PCL-5. Several multiple linear regression analyses were conducted to determine if MADM and MORi totals and NICU had a moderating effect on PCL-5 total score. The overall model for MADM was not significant (p = .053), indicating there is insufficient evidence to support the existence of a moderating effect. The overall model for MORi was significant, R2 = 0.37, F(3, 97) = 19.35, p < .001, suggesting the predictors accounted for 37.44% of variance in PCL-5 scores. Since the overall model was significant, moderation was assessed by examining the interaction between NICU and total MORi (p = .622). The results of these analyses are located Tables 40 and 41 in Appendix I. Emergency C-Section and PCL-5. Multiple linear regression analyses were conducted to determine if MORi and MADM totals and Emergency C-section had a moderating effect on total PCL-5 scores. The overall model for MORi was significant, R2 = 0.52, F(3, 33) = 11.99, p < .001, indicating the predictors accounted for 52.16% of the variance in total PCL-5 scores. However, interaction between Emergency C-Section and MORi was not significant (p = .094), indicating - - 105 that MORi does not moderate the relationship between Emergency C-Section and PCL-5. The overall model for MADM was not significant (p = .250). These results are shown in Appendix I, Tables 42 and 43. Path Analysis: “Model of Mistreatment” Finally, the study examined a model of mistreatment by assessing how well significant variables explain the relationships between mistreatment and variables of interest using path analysis, to evaluate the correlational data between the predictor variables, moderator variables, and outcomes explored above. The regression assumptions of OLS, normality, and heterogeneity of variance were assessed and met prior to running the regressions (Berry, 1993). The regressions were examined based on an alpha value of .05. First, the reliability of the analysis was tested based on the sample size used to construct the model. Next, the results were evaluated using the Chi-square goodness of fit test and fit indices. Lastly, the squared multiple correlations (R2) for each endogenous variable were examined. The results of the path analysis model are presented in Table 44, on the following page. - - 106 Table 44 Unstandardized Loadings (Standard Errors), Standardized Loadings, and Significance Levels for Each Parameter in the path analysis Model (N = 99) Parameter Estimate Unstandardized Standardized p Regressions MORI_Total → PCL5_Total -0.77(0.10) -0.76 *< .001 MORI_Total → PHQ_Total -0.19(0.03) -0.59 *< .001 MADM_Total → PHQ_Total 0.16(0.05) 0.31 *.004 MADM_Total → PCL5_Total 0.40(0.15) 0.25 *.008 ACE_Total → MADM_Total -0.86(0.34) -0.19 *.013 MADM_Total → MIBS_Total 0.10(0.05) 0.24 *.039 MORI_Total → MIBS_Total -0.08(0.03) -0.32 *.006 Caucasian Yes/No → MORI_Total 7.27(2.15) 0.25 *< .001 Childbirth_Class_Lifetime → MORI_Total -3.01(2.26) -0.10 .183 Unwanted_Interventions → MORI_Total -12.87(2.46) -0.45 *< .001 Unwanted_Interventions → MADM_Total -6.61(1.65) -0.37 *< .001 Covariances ACE_Total and MI_Hx 0.28(0.09) 0.32 *.003 PCL5_Total and PHQ_Total 16.43(3.77) 0.49 *< .001 MADM_Total and MORI_Total 39.83(8.61) 0.53 *< .001 Childbirth_Class_Lifetime and Birth_Plan 0.03(0.02) 0.18 .076 Unwanted_Interventions_ and Birth_Plan 0.03(0.02) 0.19 .060 Errors Error in MI_Hx 0.25(0.04) 1.00 < .001 Error in PHQ_Total 12.21(1.74) 0.77 < .001 Error in PCL5_Total 93.25(13.25) 0.58 < .001 Error in ACE_Total 3.16(0.45) 1.00 < .001 Error in MIBS_Total 8.99(1.28) 0.93 < .001 Error in Caucasian Yes/No 0.18(0.03) 1.00 < .001 Error in Birth_Plan 0.18(0.03) 1.00 < .001 Error in Childbirth_Class_Lifetime 0.16(0.02) 1.00 < .001 Error in MORI_Total 112.79(16.03) 0.73 < .001 Error in Unwanted_Interventions 0.19(0.03) 1.00 < .001 Error in MADM_Total 50.94(7.24) 0.83 < .001 Note. χ2(39) = 56.34, p = .036. - - 107 Assessing the Model Fit. A Chi-square goodness of fit test was conducted to determine if the path analysis model fits the data adequately. The results of the Chi-square goodness of fit test were not significant, χ2(39) = 56.34, p = .036, suggesting that the model fit the data well. This test is sensitive to sample size, which causes the test to almost always reject the null hypothesis and indicate a poor model fit when the sample size is large (Hooper et al., 2008). Therefore, given the small sample size, the results of the Chi-square goodness of fit test are considered reliable. Along with the Chi-square goodness of fit test, the root mean square error of approximation (RMSEA) and the comparative fit index (CFI) were used to assess the model fit. The CFI was between .90 and .95 (CFI = 0.92) suggesting that the model is not misspecified and the fit is acceptable (Hooper et al., 2008). The RMSEA index was less than .08, RMSEA = 0.07, 90% CI = [0.02, 0.10], which is indicative of a good model fit (Hooper et al., 2008). Finally, factor analysis requires a large sample size to construct repeatable and reliable factors. A sufficient sample is estimated by a ratio of overall sample size to the number of parameter estimates. The lowest recommended acceptable ratio for path analysis is 5:1 (Bentler and Chou, 1987). The participant to item ratio for this analysis was approximately 3:1, thus the results from the present analysis should be considered in light of this limitation. Please see Table 45 for fit indices. Table 45 Fit Indices for the path analysis model CFI RMSEA χ2(39) 0.92 0.07 56.34, p = .036 Note. RMSEA 90% CI = [0.02, 0.10]. The regressions in the model can be assessed by examining the R2 value of each endogenous variable. The R2 value identifies how much the variable is explained by the regressions - - 108 in the model. An R2 value ≤ .20 suggests the endogenous variable is not adequately explained by the regression(s) in the model (Hooper et al., 2008). The R2 values shown in Table 46, below, demonstrate that three of the five endogenous variables (PHQ-9, PCL-5, and MORi) have R2 values that are greater than .20, indicating that the regressions using these variables sufficiently explain the variance in the model. Two variables (MADM and MIBS) have R2 values ≤ .20 demonstrating that regressions with these variables only partially explain the variance in the modelled relationships. Table 46 Estimated Error Variances and R2 Values for Each Endogenous Variable in the SEM model. Endogenous Variable Standard Error R2 PHQ_Total 12.21 .23 PCL5_Total 93.25 .42 MIBS_Total 8.99 .07 MORI_Total 112.79 .27 MADM_Total 50.94 .17 Path Analysis Results. The final path analysis node diagram is presented in Figure 11, below. The figure only depicts relationships between certain variables that the path analysis identified best fit the data and sufficiently explain the relationships within the model. For this reason, not all variables of interest are included in the final model. The path analysis also illustrates regressions and correlations that significantly explain the relationships between variables, depicted using red and blue arrows respectively. The standardized unexplained variance in each variable, meaning the amount of variance in the variable not explained by the model, is depicted using grey arrows. - - 109 The results of the path analysis reveal several significant correlations and regressions, some of which were not elucidated in the multiple linear regressions in Aims 2 and 3. This model evaluates the significance and strength of relationships between endogenous variables, considering both the direct and indirect effects of all the variables in the model. Therefore, significant relationships not identified by single regression models were revealed when systemically evaluated and modeled simultaneously. This arguably provides a more representative illustration of the real-life system of mistreatment . Figure 11 Node diagram for the path analysis model with key. - - 110 Mental Health and Mistreatment. The path analysis found that the total score on MORi significantly predicted total PCL5 scores (p < .001), and total PHQ scores (p < .001), indicating that a one-unit increase in MORi decreased the expected value of the PCL-5 and PHQ scores by .77 and 0.19 units, respectively. This means that as birthing people experience more respect in their maternity care, their depression and PTSD symptoms decrease. This demonstrates the significant inverse relationship between respect in maternity care and mental health, in the context of the modeled variables. The total MADM score also significantly predicted the total PHQ scores in this model (p = .004) as well as PCL-5 scores (p = .008). However, in this relationship a one-unit increase in MADM increased the expected value of PHQ and PCL-5 by 0.16 and .40 units respectively, suggesting that an increase in autonomy was correlated with a small increase in postpartum mental illness. This demonstrates that the two different measures of mistreatment have slightly divergent relationships to postpartum mental health, when modeled within a system of risk and protective factors. With respect to the Adverse Childhood Experiences scale (ACE), the path analysis identified that scores on this measure significantly predicted the total MADM scores (p = .013). A one-unit increase in the total ACE score decreased the expected value of MADM by 0.86 units. In other words, the more adverse childhood experiences a participant endorsed, the less autonomy they experienced in their decision making in maternity care. While the regression analysis did not find a relationship between a history of mental illness and mistreatment in childbirth, the path analysis illustrates that there is a significant correlation between history of mental illness and ACE scores (p = .003). This relationship then acts upon MADM scores as demonstrated by the significant results of the regression analysis between ACE and MADM scores (p = .013). This - - 111 demonstrates how ACE and mental health history may together predict mistreatment, but mental illness alone is not significant in doing so. The model depicts that mental illness history alone did not have a direct relationship with postpartum mental illness. However, the model shows that mental illness history correlates with ACE scores, which in turn influences both MORi and MADM through their significant correlation. Together, the indirect relationship between mental illness history and mistreatment influences postpartum mental health, but only in the context of trauma history. This suggests that mistreatment may mediate the relationship between mental health history and postpartum mental health, however causation cannot be established in the present model. Further, the path analysis revealed a significant relationship between mistreatment and mother-infant bonding not found in the multiple linear regression. Total MORI scores significantly predicted the total Mother Infant Bonding Scale (MIBS) scores (p =.006), such that a one-unit increase in MORI decreased the expected value of MIBS by 0.08 units (Max = 15, SD = 3.13). Put simply, as respect in maternity care increased, mother-infant bonding improved. Conversely, the MADM score predicted the inverse; a one-unit increase in MADM increased the expected value of MIBS by 0.10 units (p = .039). Race and Respect. Race significantly predicted total MORi scores ( p < .001). This indicates that Caucasian participants experienced significantly more respect in maternity care such that as Race moved towards the Caucasian category, the expected value of MORi increased by 7.27 units. Therefore, the path analysis illustrates that participants who identify as Caucasian endorsed significantly more respect in childbirth and maternity care than participants who identify as Black, Asian, Latinx, Hispanic or mixed race. Similar to the findings of the moderation analyses, - - 112 there were no direct or indirect variables moderating or acting upon this relationship: race directly impacts mistreatment. Unwanted Interventions and Mistreatment. The Unwanted Interventions measure significantly predicted total MORi scores ( p < .001), such that a one-unit increase in Unwanted Interventions decreased the expected value of MORi by 12.87 units. In other words, as unwanted interventions in labor increased, mothers’ perception of respect from care providers decreased by an entire standard deviation (Max = 78, SD = 12.36). Similarly, unwanted interventions significantly predicted MADM scores (p < .001), indicating a one-unit increase in unwanted interventions decreased maternal autonomy in labor by 6.61 units, also nearly an entire standard deviation (Max = 37, SD = 7.06). Childbirth Knowledge. Finally, a history of childbirth education did not significantly predict total MORi scores (p = .183) within the present path analysis model. However, when these variables were removed, the entire model no longer fit the data. This suggests that while childbirth preparation does not directly impact mistreatment experiences, it does explain some variance in the overall model of mistreatment. This illustrates how childbirth classes and birth plans indirectly contribute to the interactions between mistreatment, unwanted interventions, mistreatment and postpartum mental health Qualitative Methods Results Participants Following completion of the online survey, participants were given the opportunity to participate in a qualitative interview regarding their childbirth experience. Eight participants (n = 8) who completed the survey agreed to participate in a 1-hour long, individual, semi-structured, - - 113 qualitative interview conducted live over the Zoom platform. The semi-structured interview guide may be found in Appendix G. Descriptive Statistics The qualitative data sub-sample resembles the full study sample. In the qualitative sub-sample, the participants ranged in age from 32 years-old to 41 years-old, with the median age of 36 years-old. On average, the sample was 5.72 months postpartum at time of the interview, ranging from two and a half months postpartum to 10.66 months postpartum. See Table 47. Table 47 Descriptive Statistics of Interview Data: Demographic Means Variable (n = 8) M SD Min Max Age (in years) 36.66 3.24 31 41 Birth Rate 1.37 0.52 1.00 2.00 Time Since Index Birth (in months) 5.72 2.64 2.16 10.66 Infant Weight (in pounds) 6.38 1.94 3.31 8.43 Six participants identify as Caucasian, one participant identifies as Black, and one participant identifies as Asian. With respect to religion, half of the sample identify as Agnostic (n =3) or Atheist (n = 1), while the rest of the sample identify as Reform Jewish, Orthodox Jewish, Hindu, and Christian . Six participants reside in Brooklyn, one lives in Manhattan and ones lives in the Bronx. All participants reside in middle to high-income households, most are professionals working in law, medicine, engineering and academia. These data are described in Table 48. - - 114 Table 48 Interview Descriptive Statistics: Demographic Frequencies Characteristic (n = 8) n % Race/Ethnicity Caucasian 6 75.00 Black 1 12.50 Asian 1 12.50 Religion Agnostic 3 37.50 Atheist 1 12.50 Jewish (Reform) 1 12.50 Jewish (Orthodox) 1 12.50 Hindu 1 12.50 Christian 1 12.50 Borough of Residence Brooklyn 6 75.00 Bronx 1 12.50 Manhattan 1 12.50 Annual Income >150,000 6 75.00 90-150,000 2 25.00 Education K-12 8 100.00 Higher Education 8 100.00 Currently Employed Yes 6 75.00 No 2 25.00 Partner or Spouse Yes 8 100.00 Five participants were primiparous, two participants had babies in the NICU, and one participant gave birth to twins. 62.5% of this sub-sample reported that they received an unwanted intervention during their birth (n = 5), and 62.5% were induced. All participants gave birth at Manhattan hospitals. Please see Table 49 for descriptive statistics of perinatal variables. - - 115 Table 49 Descriptive Statistics of Interview Data: Perinatal Variables Variable n % Primiparous 5 62.50 Childbirth Class Index Pregnancy 4 50.00 Lifetime 5 62.50 Birth Plan Made a Birth Plan 6 75.00 Shared Birth Plan with Providers 6 75.00 Birth Plan Followed by Providers 3 37.50 Birth Companion Partner 4 50.00 Partner & Doula 4 50.00 Health Insurance Private Insurance 8 100.00 Hospital Birth Borough Manhattan 8 100.00 Perinatal Health Variables BMI >30 0 0.00 Gestational Diabetes 0 0.00 Hypertension 3 37.50 COVID-19 Positive at Birth 0 0.00 Cesarean Section 3 37.50 Planned Cesarean Section 1 12.50 Emergency Cesarean Section 1 12.50 Health Complications Hemorrhage 0 0.00 Preeclampsia 1 12.50 Medical Interventions in Labor Epidural 4 50.00 Induced 5 62.50 Episiotomy 0 0.00 Unwanted Intervention 5 62.50 Infant Health Outcomes NICU 2 25.00 >1 Week on NICU 1 12.50 Twins Birth 1 12.50 - - 116 With respect to quality of treatment in maternity care and childbirth, the sample had an average Mothers on Respect Index (MORi) score of 64.5, indicating Moderate Respect and scores ranged from 43 (Low Respect) to 78 (High Respect). The average score on the Mother’s Autonomy in Decision-Making scale (MADM) was 28, indicating Moderate Patient Autonomy, on average. MADM scores ranged from 13 (Very Low Patient Autonomy) to 37 (High Patient Autonomy). These scores are shown in Table 50. Table 50 Summary Statistics Table for Interview MADM and MORi Scores Variable M SD Min Max MADM_Total 28.00 7.06 13.00 37.00 MORI_Total 64.50 12.36 43.00 78.00 On average, interview participants did not endorse mental illness, major adverse childhood experiences, or impaired mother-infant bonding. They did endorse moderate to high levels of perceived racism. Please see table 51, below. Table 51 Summary Statistics Table for Interview Mental Health Variables of Interest Variable M SD Min Max PHQ_Total 3.87 3.18 0.00 10.00 PCL5_Total 6 6.14 0.00 15.00 ACE_Total 1.25 2.38 0.00 7.00 PR_Total 56.50 7.29 49.00 67.00 MIBS_Total 2.88 2.29 0.00 6.00 - - 117 Final Codebook The coding team, composed of four trained and qualified coders, completed eleven iterations of the codebook over the course of five months, before the final codebook was completed and all eight transcripts were coded a final time, as determined by saturation and ICR’s above k=.60 for all transcripts. Codebook Reliability The results of the kappa reliability analysis provide evidence that the qualitative methodology process was both rigorous and reliable. Theoretical saturation occurred when no new codes were added or removed from the codebook (MacPhail et al., 2016). Further, the present study added a novel indicator of saturation byway of a predetermined reliability statistic. Therefore, the second indication of saturation was when ICR had increased significantly enough to indicate substantial coder agreement (k>.60). The degree of intercoder reliability (ICR) increased with each iteration of the coding process, as measured by changes in the kappa statistic. By these criteria, saturation was reached at codebook iteration #11, with an ICR of k =.67 indicating “substantial” agreement. This total kappa statistic for the final iteration of the codebook (all codes included) indicates that the codebook demonstrates substantial intercoder agreement and consistency. These results are shown in Table 52. Table 52 Summary Table for Total Cohen’s Kappa Intercoder Reliability (ICR) Variable M Interpretation Final Kappa (All Coders, All Transcripts) k=.64 Substantial Agreement Final Kappa from Highest ICR k=.67 Substantial Agreement - - 118 Codebook Hierarchy & Structure The final codebook was composed of eight parent codes: (1) “Autonomy,” (2) “Best Practices/Standards of Care,” (3) “Failure to Meet Professional Standards,” (4) “Hospital or Health System Conditions and Factors,” (5) “Knowledge and Preparation,” (6) “Poor Rapport between Patients and Providers,” (7) “Understanding Mistreatment,” and, (8) “Unequal/Unfair Treatment.” Each parent code possessed up to seven sub-codes and each of those had zero to four sub-sub nodes. In the process of stepwise coding, each sub-code and sub-sub code weas assigned a descriptive name and working definition, which evolved through each iteration of coding, agreed upon by all coding team members (O'Connor & Joffe, 2020). Please see Appendix H for full codebook, nodes, and definitions. Process-Level Outcomes: ICR and Process Data Code-Level Kappas. Codes with latent meaning material predominated by perceptions or abstract concepts had the lowest ICR values, such as the code sub-code “Vulnerability” with a k = .47, located under the parent code of “Autonomy.” While codes containing explicit and descriptive meaning material, such as concrete facts or outcomes, had higher ICR values. An example of this is the sub-sub-code “Provider Changes” with an ICR of k =.93, located under the parent code “Hospital or Health System Factors” and under the sub-code “Constraints.” With respect to process, ICR improved through increased and targeted coder training, refinement of coding rules throughout the coding process, collaborative team-based discourse, and delimiting units of measurement utilized by all coders. Transcript #1 consistently had the highest intercoder agreement with a high of k=.74. Transcript #3 had the lowest with a high of k=.64. Table 53 depicts the Cohen’s Kappa for the final iteration of each transcript, illustrating the degree of coder agreement. - - 119 Table 53 Intercoder Reliability (ICR) Cohen’s Kappa Final Measures by Coding Group The results of this stepwise approach to codebook development and thematic coding process indicate that the codebook, codes and the process itself were found to be reliable, and that a satisfactory level of intercoder consensus was achieved. Finding that the codes and coding process were reliable, the thematic analysis was conducted. Thematic Outcomes Coders reached areas of high convergence (k>.60) in multiple thematic areas. Participants in this study reported perceived variability and inconsistency in the delivery of maternity care in the hospitals at which they birthed. The participants described diverse examples of mistreatment in childbirth, as defined by the 7 Dimensions of Mistreatment identified by Bohren and colleagues. The themes identified in the present analysis fall under the following categories of mistreatment: - - 120 stigma and discrimination, verbal abuse, failure to meet professional standards of care, poor rapport between women and providers, health system conditions and restraints (2015). The reflexive thematic (RT) analysis utilizing a Phenomenological and Critical Feminist lens gave rise to nine themes that emerged from the data: (1) Discrimination and Unfair Treatment, (2) Confusion and Abandonment, (3) Disregard for Patient Autonomy, (4) Hospital-Level Drivers of Mistreatment, (5) Women Treated as Passive Participants in their Own Birth, (6) Acceptance and Normalization of Mistreatment, (7) Self-Advocacy and Vulnerability and, (8) Reclaiming Power through Knowledge. These themes are explored in-depth below, with supporting data in the form of quotes. Figure 12, below, illustrates a map of these themes. Figure 12 Thematic map showing the eight main themes and sub-themes. Theme 1: Discrimination and Unfair Treatment Inconsistent and variable care delivery was the most consistently reported phenomenon participants experienced in their New York City hospital birthing experiences. The participants - - 121 observed that the variability in care was not due to different medical needs or based on evidence-based standards of care, but rather on their individual social and demographic factors. One birthing person who identifies as Black experienced racial discrimination from care providers, often during the most vulnerable and painful stages in her birth: “But when it was time to move from the triage to the labor room, the nurse was like, ‘Okay well, you can walk.’ I was like ‘I'm in the middle of giving birth, no, I can't walk.’ And, it wasn't as though that it's their policy to have people walk, like there was a white person who had gone before me, and she had no problem with getting the bed and helping her…” “I think there's just a lot of like pre-set ideas about black women, black moms, black birthing people…And when they see black first, they automatically assume you are without a spouse, and that you will need the WIC forms, that you want formula…There was also a question of whose name would be on the birth certificate…” Participants also observed differential treatment between patients based on their social class or insurance status: “I told you I was on Medicaid at the time…it was an Upper East Side practice, you could see the high, fancy looking people coming in. And I noticed that he would, sometimes I would be waiting, I would come in before them, and then they would be seen before me. There was a lot of that going on. I think he treated certain people better than others.” “So yeah, for me personally, I think the insurance thing makes a huge difference. This time I had private insurance… and it's just night and day, and I actually work at a state hospital. It's sad, I mean you see what people get on Medicaid and it is not the same quality, it's part of insurance.” Finally, birthing women felt that there was a randomness in the quality and consistency of care, noticing that while there was inequity and discrimination, there could also be special treatment which felt like trying to “game the system” or get “lucky:” “There are people who really shine through because they were present, answered questions, didn't treat me like I was kind of an afterthought or a hassle. But on the whole, it just continued to feel a bit like those were lucky breaks to catch those people, but that it was not the norm.” - - 122 “And I remember the lactation consultant being so helpful and being like, ‘Here's what you have to do. I'm gonna go and try to get a private room for you. But you gotta try this,’ you know. Like you had to game the system.” Theme 2: Abandonment and Confusion Themes of feeling and being abandoned for long periods of time during childbirth and feeling confusion around what is happening were prominent in the data. Many participants felt all alone during their birth at the hospital, not certain when providers would return, who to ask for help, and where to seek physical and emotional support and medical advice. The word “abandoned” was a high-frequency word throughout transcripts. The data illustrate the profoundly painful juxtaposition of feeling lost and alone during any medical procedure, but note it's especially difficult during childbirth, a time when they are seeking guidance and constant support: “My sister's a doctor, and anytime I've talked about being frustrated with my doctors…she is like, ‘You do not know how burned out all doctors are.’ So I understand how it comes to be that way that you feel a little bit like an abandoned ship. But with labor it's just so hard to feel that way…like there's not anybody in charge.” “When I was pushing, people would come in and help me push. And then I remember this woman being like, ‘Oh, I have to go on my rounds, good luck!’ And would leave between my legs, and there would be nobody there. And I would be like, ‘Wait! Am I…who, am I supposed to…what do I do now?” Some of these challenges were a function of the hospital system. For example, shift changes felt abrupt and led birthing women to feel abandoned, especially when it was a provider who had been helpful or kind to them: “And I just felt so despairing, I just, I really felt like I couldn't figure it [giving birth] out, and I wasn't going to be able to do it. And then this other OB came in. It was a shift change, right? So the doctor was like, ‘I gotta go,’ and I was like, ‘Please, please, please, can you stay like, please? Can you deliver this baby?’ And she was kinda like, shrug, like ‘It's shift change,’ you know.” This often was accompanied by a sense of confusion, whereby information was not being shared with patients and likewise patients did not know to whom to direct their question, or to whom to - - 123 speak about requested changes in their care. As a result, patients felt scared and alone, which increased concern for their own safety and feelings of anxiety: “And I was sort of like, I'm not out of bounds to just be asking what the fuck is going on. And I keep seeing a different person every 4 hours, and it doesn't really feel like information is being transferred that well from person to person.” “I feel like finding a way for there to be more information disseminated more clearly. I didn't need to be handheld, but I would have liked more facetime with anybody, you know, like a resident or a doctor. I just felt left alone so much for such long stretches.” “So it felt like that endless waiting and just uncertainty, and not knowing what's going on… I kind of blocked it out almost just because, I see it in flashes.” Long-periods of waiting coupled with poor communication around shift changes impaired appropriate pain management and increased concern for their health and their infant’s health. Patients often felt unable to get the answers they sought or medical attention that they needed: “I remember vividly that it was supposed to be 6 hours Tylenol, 6 hours Motrin, and we had to go get them every time. All of a sudden I'd be like, ‘Oh, my God, it's been 10 hours, and I'm starting to be in pain!’ No one came.” “I was like, ‘Is the baby getting enough to eat, is the baby okay?’ And this nurse literally was walking out of our hospital room and was like, ‘No, she's not getting enough.’ And then just walked out. And I was like what?” Doulas compensated for the lack of support from the treatment team and helped to counteract this sense of confusion and abandonment. Some participants felt regretful that they did not have one and chose to hire one for their second birth: “I feel bad saying this because I think this is a workaround, but my advice to women giving birth in a hospital is to have a doula, because that's the best solution to the fact that you feel essentially abandoned until you're in very, very, very active labor.” - - 124 “My OB was like, ‘It's a waste of money, get a night nanny.’ But I felt like the doula became the through-line for us. She had seen this before. She had experience and expertise… She was…with us the whole way through…” Theme 3: Disrespect and Disregard for Patient Autonomy The data illustrate a pattern of disrespect and disregard for the choices, preferences and rights of the women interviewed. Their autonomy was challenged and violated through the administration of unwanted interventions and a failure to provide full and informed consent. In many cases, patients were not aware their autonomy was violated until they reflected on their birth experience or spoke with other birthing people: “I had to sign a bunch of things on the iPad to give consent… for the C-section. I am a lawyer so I am trained to read the fine print, but I was in no condition to read the fine print.” “Once I was in and we had said yes to that [the Pitocin], they just sort of… continued. I told them I didn’t need more but they just kept cranking it up…” Many patients felt pressured by providers who challenged their choices or insisted on a certain intervention to the point where patients felt they could not refuse. Some felt the treatment team took advantage of them by attempting to pressure them into accepting procedures they did not want, while in active labor: “I had to really stand my ground against some interventions that I was very clear beforehand that I didn't want, and they still tried to push them onto me in the delivery room! … I said no. And it's really unfair for you to be asking me this again and again at such a critical moment.” “They started to do things that I wasn't super stoked about. In retrospect, it was my birth, and I probably could have been more vocal, and they couldn't do anything I said ‘no’ to... but I felt like at some point they weren't budging, and that I had to start Pitocin.” In some cases, patients were threatened that if they did not undergo a certain intervention, something bad would happen to them or their baby. Others were told that they would be given - - 125 certain interventions they did not want, if they did not progress fast enough leading to increased stress and fear: “Looking back I feel like it may have been a little aggressive. They broke my water pretty early….once they break your water, you're on a clock. So I felt a little bit stressed from the whole thing. They were like, “we have to get you an epidural, because if anything goes south, then we're gonna have to intubate you.” I didn't want the epidural…But they were very aggressive with me, and I mean eventually I did it.” “They said [if I don’t progress] they would schedule me for a C-section which was stressful for me, because it was the exact opposite route that I wanted to go…It felt like a lot of pressure from my doctor.” For one patient who spoke up for herself, verbal threats were then addressed to her husband when she did not acquiesce to an intervention: “They tried to tell my spouse when I said no [to pitocin], “Oh, are you really gonna be okay if she bleeds to death because she didn't want to do this?” Some participants felt that being pressured into things they did not want was part and parcel of having a hospital birth: “Depending on the hospital…they might try to push you into stuff that you don't want. And if it's not working on you, they might then try to push your partner … So if you'd prefer not to have to deal with that while you're trying to give birth then maybe another setting is better for you.” Theme 4: Hospital-level Drivers of Mistreatment Systemic hospital-related factors and constraints posed challenges to getting the birth women sought and comprised “drivers” of mistreatment in the hospital. Patients found that policies, procedures and variability in care introduced safety risks into their births that they did not anticipate. While many women sought out hospital births to reduce risk to themselves and their infants, they encountered unanticipated challenges as a result of system-wide practices, such as provider changes. Patients felt the lack of communication between providers to smooth these transitions negatively impacted their care and sense of safety: - - 126 “One of the most frustrating parts about the hospital to me was that you're never seeing the same person. So, for example, when someone's checking how far dilated you are… it's not a standard measurement–it's with their hand. Each person has a different hand, so each person has a different measurement. And so at some point during this ordeal, I realized…it could actually be that I'm actually more dilated.” “I really wish that there was better consistency of care. Even if people are rotating and doing rounds, and even if shifts change, I wish that it didn’t feel like the first time…I wish I wasn't responsible for holding all the pieces of information and making decisions. That there was some better way of passing things on.” Other risks were presented by medical students performing procedures, “botched” interventions and administration of drugs which they were told not to administer, which increased stress for the birthing women: “I got an epidural which was good until it broke. It didn't go in correctly… I only felt it on half of my body, and it took a long time to get the anesthesiologist team to come back.” “The anesthesiologist read off just one medicine that I am allergic to and I am allergic to three. And I said you missed penicillin, and he goes, ‘Oh.’ And the OB said ‘You gave her penicillin?’ And he said yeah…And I was like, ‘Why did you do that, I am allergic to that, and we talked about this.’ He was like, ‘What’s the reaction?’ And I said ‘Anaphylaxis.’ And he said, ‘You're gonna be fine.’ And I was like ‘that's probably not for you to say!” “I'm feeling a lot, very intense pain, so finally my OB comes in, quite angry on my behalf, saying that they have given me enough booster packs of epidural to “knock out a rhinoceros,”…that's when the anesthesiologist came in and told me that the epidural was never put in the right place.” Women made attempts to reduce or respond to this risk by reducing the amount of time they spent at hospitals, or trying different hospitals hoping for a better system: “I mean…it's a matter of spending less time in the hospital, like more time in the hospital usually means, at some point, some sort of intervention will be presented.” Theme 5: Women Treated as Passive Participants in their Own Birth - - 127 Gendered power dynamics played a defining role in the childbirth experience. There were multiple forms of power revealed through the analysis including power through gender, age, and status. Implicit bias against women was revealed in the ways in which some treatment teams interacted with women, at times speaking to them using infantilizing, patronizing, or condescending language: “And I felt like, I really felt with this one particular doctor…I imagine, from their perspective that women are hysterical and demanding, and I just felt like they were sort of like, ‘Okay, this is what this is, and we're the expert,’ you know. And I was sort of like, I'm not out of bounds to just be asking what the fuck is going on.” In this way, many treatment teams assumed a position of power and women were expected to accept a “passive” rather than “active” role in their own births. When one patient refused Pitocin during her birth, she received a letter saying she was dismissed from the practice: “My friend who is an OB…he said, this is what they use when they dismiss their “bad patients,” and that's what they call them bad patients. And I’m like how paternalistic of you guys. Like, I'm not a child, this is not…I wasn't a bad patient, I was an adult, that was asserting my right to certain things.” The language, attitude and behavior treatment teams used when working with their patients, informed women’s feelings of respect, safety and autonomy in childbirth. Women felt disempowered when providers questioned their ability to birth or questioned the choices they made surrounding their own bodies “And she was just kind of looking at me like, she was like, ‘You're not pushing right, like you're not using the right muscles.’” “I also feel like in the hospital setting they both times were judgmental of my choice not to get an epidural. One said “Oh, good luck”...” Gender roles played an important part in the power dynamics in the birthing room, with many participants noting that treatment teams dismissed their pain levels when the women asked for help, but were responsive when their male partners asked for help: - - 128 “I was hysterical, which I think should have clued a doctor in to my pain level, but they just wanted to keep on trying what they had already tried. It took my husband basically snapping at them, and…saying, ‘No this is unacceptable. You have to try something different,”… and that was ultimately what got them to call in another doctor and get it right.” “They listened to him [my husband] which was fine, but also annoying because they should have listened to me, but whatever.” The most salient and concerning pattern was the systematic dismissal of women’s concerns regarding their own health, their pain levels and their bodies. It took great effort for women to get their doctor’s attention and some women experienced serious health complications as result of dismissiveness. One participant had an emergency C-section after months of complaining of symptoms of preeclampsia, but her concerns were dismissed: “And on my bi-weekly visits, I would say, like, “I'm really uncomfortable,” you know, really swollen and like I don't feel good, and they're like, yeah, you're pregnant, which you know is probably true…But I just felt like there was no follow up questions like, how are you uncomfortable? …Like, I had to stop working. There was no delving deeper into my discomfort, and it was totally just brushed off…they never had me pee in a cup, they never tested me for proteins in my urine….probably they would have spotted Preeclampsia so much sooner had they been doing that…I wish they would have been able to catch it sooner…” Theme 6: Acceptance and Normalization of Mistreatment Patients tried to “make sense” of or justify the quality of care that they received. Women minimized the psychological and emotional impacts of mistreatment, often focusing on the outcome of the birth rather than the process of the birth. Participants tended to focus on things being “alright in the end,” despite the serious medical and interpersonal violations they experienced. These attempts at minimizing and justifying their experiences had the effect of normalizing their experiences of mistreatment. - - 129 For example, one participant received a botched epidural and her pleas for assistance with her epidural were not responded to for over eight hours. Her narrative illustrates both a helpful attempt to cope as well as an acceptance of poor standards of care: “But you know what in the end it actually worked out okay, because by not having the epidural on that side I was able to like birth easier I think because I had the sensation. So, it actually worked out okay in the end.” Some women attempted to justify their experiences by taking the perspective of the physician or imagining why things might not have gone the way they had hoped: “And then I got the epidural, like I said, it did not go in correctly the first time, which I don't blame on the anesthesia team. I understand that dealing with pregnant women you can’t guide the needle with an X-ray as you normally would…I don't blame them for putting it in in a way that didn’t work.” These narratives illustrate a trend towards accepting mistreatment as a normal part of giving birth, which seems to be a function of the high value places on physical health outcomes and devaluation of the experience of the process of birth, and its emotional implications: “Even though my birth was not as I planned, and they gave me things that I am seriously allergic to and all that, I really don’t feel traumatized by it, I would still hope to give birth at [SAME HOSPITAL] in the future.” “I think for me the experience of it, and again it wasn't this time around, this time it was pretty smooth except for the fact that you know you still have some ignorant questions because of my race…like the formula thing, no real issues.” Theme 7: Self-Advocacy and Vulnerability The theme of advocacy and having to “fight” for the birth one wants while at one’s most vulnerable emerged through the analysis. Provider-patient interactions felt combative rather than collaborative to many patients, which led some to lose trust that their care providers would act in their best interest. Many chose instead to take an active role in advocating for themselves: “And then, in a way like I felt like it was like me against the doctors, you know. Like I had to beat them so they don't cut me open.” - - 130 “I kind of just felt like I had to fight so hard to get this vbac, and I don't know.” “The nurse said that she would put a note on it, that she would communicate to the other nurse…But I had zero faith if that was actually gonna happen.” Care providers and hospital staff responded variably to patients’ self-advocacy. Some women who refused the recommendations of their treatment team, or chose not to conform to hospital policies, faced backlash or retaliation: “I got a letter from them saying that I was dismissed from their practice because I had refused their medical advice for certain interventions… it made me realize that the things that I was thinking about while giving birth like, I wasn't crazy, these people are actually doing this really fucked up shit to me…And I would get it if I was like, bleeding out and about to die…Like, I said that I didn't want Pitocin and that was it, that was the big thing.” “I was so not anywhere near labor, and they gave me all these interventions, and my body wasn't ready for it. I was so upset about it….I didn't progress…He was nasty to me, a bully. I told him I really don't want a C-section. He's like, ‘Well if you don't do it I’m making you sign a paper, and I’m not responsible.’” Many participants highlighted how “unfair” it felt to be challenged on their preferences and choices while in such a vulnerable state: “I mean, it’s hard to not take it personally...it shouldn't happen [being pressured], and when you are in a place where you're super vulnerable, you've got all of these hormones, your body is like going through something super traumatic, you shouldn't have to be thinking about this…” “And I mean it was just, they were just very rude, they were just very rude and unpleasant, I don't really know how else to describe it. So I think, for people who are dealing with patients who are in their most vulnerable and sometimes painful states, that was just very off-putting.” Participants who had a difficult time with advocating for themselves tended to feel more disappointment and less satisfied with their births. Participants who were able to self-advocate, assert themselves, or have partners or doulas who did so for them, tended to receive more timely medical attention and were more successful in resisting unwanted interventions: - - 131 “I feel like in my experience, while your doctor can be helpful, it feels like what they are telling you is the rule but you don't have to do everything that your doctor suggests.” “So there's something called rhoGAM, and people who have negative blood have to take it, so that in case that baby has positive blood that your body doesn't make those antibodies. He didn't even initiate that. I know about it because I’m a medical person. But I asked like, ‘What about rhoGAM?’… I had to initiate it all myself. “It's like you want to give me something that I specifically said that I don’t want…And, when I was talking to the doctors like, ‘Oh, well, we give this to everybody.’ I was like ‘Why, why do you give this to everybody, tell me why you give it to everyone?” Theme 8: Reclaiming Power through Knowledge Knowledge comprises a determining factor in how women experienced their hospital births and often determined their actual medical outcomes. Women who possessed childbirth knowledge, knowledge of the hospital system, and good self-knowledge more successfully secured the births they wanted. Women who participated in a birth class, who spoke with a family member who had given birth, or who were multiparous reported less mistreatment and more feelings of empowerment: “And because I felt like I had a lot of working knowledge of what was going to happen and what to expect, the birthing class had actually gone a really long way in terms of helping me know what agency I had in the hospital.” “Like, there's nothing wrong with the epidural, there's nothing wrong with the C-section…but you have to educate yourself on what those options and interventions are, and see if you feel comfortable with it, and if you are then go for it. I guess your body, your birth, do what you want, and do it from an informed place.” Many birthers did not know they had rights as patients, including the right to refuse care. Patients who did not prepare for their birth or whose providers did not educate them on their options in birth, often perceived their births as more “traumatic:” - - 132 “I felt like for my first birth, you know I felt like this naive person that didn't know what was flying, and I was letting them do whatever they did.” “People kept asking me what my birth plan was, and I said I don’t know what that is. I just want a healthy baby and I just don’t know what that meant. Because I thought honestly the only decision you made really was whether or not you wanted an epidural and I wanted one. We had nothing else planned.” Knowledge of themselves, childbirth and the hospital system helped women to manage their expectations, refuse unwanted care and navigate the hospital system. Women expressed that being in touch with their own preferences, comfort-levels and personality helped them to feel more prepared for their hospital birth, less uncertain and more empowered: “You gotta know yourself…if you're not going to be able to withstand those kinds of pressures, make sure you're in a place where they're not gonna do that to you…But hospitals do come with their own rules and their own expectations…so if you're not comfortable with that kind of thing, really think about who you are, what you want and what this place does.” Triangulation of Qualitative and Quantitative Results The qualitative themes identified by the thematic analysis largely support the findings of the path analysis and help to explain the phenomenon observed in the data with regards to childbirth experience, respect and autonomy in labor, and the effects of childbirth knowledge and preparation on mental health and medical outcomes. Together, the results of the path analysis and the thematic analysis were synthesized into a mixed-methods and multi-medium data model to demonstrate this complex and dynamic correlational and observational data illustrating and “giving voice” to the experience of a hospital birth in New York City, in line with phenomenological theoretical approaches. This triangulation approach combines the statistical power of path analysis with the findings of the Reflexive Thematic (RT) analysis and is modelled from a Causal Loop Diagram (CLD) (Braun, Clarke & - - 133 Hayfield et al., 2019; Haraldsson, 2004). This mixed-methods, mixed-data, and interdisciplinary model is shown in Figure 13. Figure 13 Triangulated quantitative and qualitative results rendering a mixed-data and mixed-method Model of Mistreatment The mixed-methods model provides statistical context for interpreting the themes that arose in the qualitative analysis. As such, one can contextualize the significance of themes by drawing on the statistical relationships and significance between the correlations and regressions that form between the constructs underlying the qualitative themes. Scores on measures of mistreatment show, on average, moderate levels of respect and autonomy during labor. Even so, those who endorsed moderate respect and autonomy experiences still indicated that they received unwanted interventions during their childbirth. The statistical - - 134 analyses revealed that this was correlated with mistreatment, such that those who received unwanted interventions in labor were more likely to report lower levels of respect and autonomy during their labor. These statistical results were supported by the qualitative findings. The administration of unwanted interventions or being pressured by providers into accepting unwanted interventions decreased patient trust in the treatment team (i.e.,“…having to really stand my ground against some interventions...”). This loss of autonomy led to feelings of disappointment and regret, reflected in the significant correlation between mistreatment and mental health in the path analysis (i.e., “I know, like in retrospect, that it was like my birth, and I probably could have been more vocal... but I felt like at some point they weren't budging, and that like I had to start Pitocin.”). Along these lines, the statistical analyses identified a relationship between mistreatment in labor and postpartum mental health outcomes, holding for mental health history. This finding is supported by the qualitative themes, which identify the emotional consequences of mistreatment including confusion and hopelessness (i.e., “I just felt so despairing…”). Some mistreatment experiences that contributed to this were abandonment and lack of consistency in care (i.e., “…left alone for such long stretches…”). While interview participants did not directly endorse depression or PTSD symptoms because of their birth, which differs from the quantitative data, they used words with high emotional-loading to describe their birth, including ‘traumatic,’ ‘scary,’ and ‘abandoned.’ These first-hand accounts allude to the powerful negative emotional impact of mistreatment, which as illustrated by the path analysis, can develop into psychopathology. The quantitative findings identify that Black, Latinx or Hispanic are at an increased risk for mistreatment in maternity care. This finding is illustrated by the qualitative data which provides examples of how Black patients were discriminated against as demonstrated by rude staff - - 135 interactions, racial microaggressions, inequitable medical care, and dismissal of the patient’s preferences and choices, as compared to her Caucasian counterparts (i.e., “…there’s a lot of pre-set ideas about black women, black moms, black birthing people…”). Patients felt they were treated differently due to their race, and Caucasian participants observed differential treatment in other patients due to their race or ethnicity. Similarly, the qualitative data illustrate how patients with Medicare felt that they were exposed to more surgical interventions and that private insurance holders received preferential treatment. These negative staff interactions led to poor provider relationships and feelings of decreased autonomy in labor and increased stress (i.e., “In a way like I felt like it was like me against the doctors, you know. Like I had to beat them so they don’t cut me open.”). However, this finding was not supported by the results of the path analysis or regression analyses, which found that there was no difference in mistreatment by insurance type. Therefore, while patients experienced several forms of unfair treatment and discrimination, racial discrimination was significantly associated with mistreatment and unwanted interventions. Finally, the statistical analyses revealed a correlation between childbirth knowledge and preparation, unwanted interventions, and respect in maternity care. The thematic analysis provides support for this (i.e., “…, the birthing class went a long way in helping me to know what agency I had in the hospital”). Namely, those who had more knowledge of how the hospital system works and who understood their patient rights had a greater satisfaction with their birth, their providers, and more confidence in themselves (i.e., “In a New York City hospital you are agreeing to follow by a medical model… it's important to advocate for yourself but also to be flexible…know your preferences…”). - - 136 While the relationship between childbirth preparation and mistreatment was not significant in the path analyses, these qualitative data reveal a de facto interaction between childbirth knowledge and childbirth experience. Patients perceived knowledge to be an important determining factor in hospital birth experience. Therefore, despite the absence of direct effects, the qualitative data elucidate the important indirect influence knowledge and preparation has on the overall mistreatment model. - - 137 CHAPTER VI: DISCUSSION Main Findings Mistreatment in Maternity Care The present study identified and described the prevalence and forms of mistreatment in maternity care and revealed several significant relationships between mistreatment and childbirth preparation, race, and mental health among a sample of people who gave birth in New York City hospitals. On average, the study identified that most of the sample (40-60%) did not endorse mistreatment experiences; most of the sample experienced Moderate to High levels of respect and autonomy in decision making in their maternity care and labor. However, on average, 10-15% of the sample experienced mistreatment in the form of Low to Very Low respect and/or autonomy in maternity care. These findings suggest that on average, women in this sample experienced similar levels of mistreatment in New York City hospitals as the approximate estimate for the United States on average, which is 17% according to recent studies utilizing the same measures (Bowser & Hill, 2010; Vedam et al., 2019; Diaz-Tello, 2016). There were several areas of mistreatment measured by the Mothers on Respect Index (MORi) and Mother’s Autonomy in Decision Making (MADM) scale that were highly endorsed by the sample. Patients commonly reported that they refrained from asking questions due to concern they may be perceived as “difficult” by providers. They also felt they did not have enough information to make informed decisions about their medical care or were not given the appropriate opportunity to make an informed decision. These violations of patient autonomy included patients not being offered different treatment options for the maternity care they received, patients not being given enough time to thoroughly consider different care options in labor and delivery, and - - 138 providers not explaining the advantages or disadvantages of different maternity care options. These findings provide support for existing literature which identifies that lack of respect for the patient’s autonomy over her own birth and decisions surrounding her care is a common form of mistreatment in American childbirth (Sega et al., 2021; Bohren et al., 2015; Gebremichael, 2018; Burrowes et al., 2017; Wolf & Charles, 2018; Vedam et al., 2019). The study identified several other forms of mistreatment. These include: the administration of unwanted procedures, pressure to undergo procedures, verbal threats from providers, racial discrimination, abandonment for long periods of time, medical neglect and poor pain management. Together, these forms of mistreatment fall under the following four domains of mistreatment observed by Bohren and colleagues: stigma, verbal abuse, failure to meet professional standards of care, poor rapport between women and providers, and health system conditions and constraints (Bohren, 2015). The frequency, forms and experiences of these categories of mistreatment will be discussed in the following sections. Risk and Protective Factors for Mistreatment The study identified that unwanted interventions in labor, maternal race and parity were the most significant risk factors of mistreatment. The effects of unwanted interventions on mistreatment were moderated by both having a birth plan and one’s race, while no moderators were identified between the relationship between race and mistreatment. These relationships are put into context of the current literature and explored at-length, below. Unwanted Interventions. The present study found that the most significant predictor of mistreatment in maternity care was having an unwanted intervention in childbirth. In the present sample, 25.23% of respondents indicated that they received an intervention that they did not want during their labor. - - 139 Those who received an intervention they did not want reported receiving less respect from providers and having less autonomy and power over decision making in their own labor. Participants described several examples of unwanted interventions, including Pitocin, providers administering more Pitocin after the patient explicitly said they did not want more, and emergency Cesarean sections that patients felt they did not need and did not want. These experiences of unwanted interventions decreased the patients’ sense of control over in their own labor, and increased distress surrounding the childbirth experience. Birth Plans Make a Difference. While unwanted interventions were a significant risk factor for mistreatment, the study found that birth plans helped to protect against this effect. The negative effects of unwanted interventions on one’s autonomy in labor increased among those who did not have a birth plan. This suggests that while unwanted interventions negatively impact autonomy in maternity care, patient autonomy is even lower when one does not have any form of birth plan, among those who have an unwanted intervention. This finding provides support for the relationship between childbirth preparation and mistreatment prevention. Interestingly, the presence of a birth plan strengthened the negative effects of unwanted interventions on autonomy in labor, suggesting that awareness of one’s rights in labor might serve to increase one’s awareness of when autonomy is being violated. Research on the effectiveness of birth plans in increasing autonomy in labor has, until this point, been mixed. Some studies suggest that birth plans help to reduce childbirth fear and pain in labor, while others indicate it has no impact on autonomy or decision-making processes (Lundberg, Berg & Lindmark, 2003; Whitberg & Hillman, 1998). The findings of the present study help to establish the significant interaction between birth plans and autonomy in labor and determined that the absence of a birth plan negatively impacts treatment in maternity care and the presence of a - - 140 birth plan may increase one’s perception of violations of personal autonomy, when unwanted interventions are administered. Despite the effectiveness of birth plans in moderating mistreatment in childbirth, one-third of participants who made a birth plan did not share it with their provider. And, of those who did make and share a birth plan in the study, only about 69.7% of them found that their provider followed their birth plan. This finding may help to illustrate why some participants felt that making a birth plan was not worthwhile. Participants reported that some providers told them that making a birth plan was not “worth the time.” These contrarian attitudes towards birth plans may impact the implementation of birth plans, despite the patient’s best efforts to create and share one. Further, 36.94% of the sample indicated that they held back from asking questions or discussing their concerns with their provider, because “I thought my doctor or midwife might think I was being difficult.” This may help to explain the lack of birth plan implementation, such that those who do have a birth plan may still feel unable to speak-up when their birth plan is not being implemented, for fear of being negatively perceived by providers. Contrary to the sentiments of some patients and providers who do not believe birth plans make a difference in their births, the findings of this study concretely demonstrate that having a birth plan increases one’s autonomy and respect in birth, no matter the actual medical outcomes. Birth plans were associated with less mistreatment in maternity care. Qualitative findings supported this; women who did not have a birth plan more often experienced unwanted interventions, while those who implemented a birth plan described a greater sense of control over their birth. Together, these findings demonstrate that birth plans are a powerful avenue for preparation, for demonstrating control to oneself and for ensuring important conversations around - - 141 patient options, rights and expectations are had with the provider and communicated between treatment team members. Racial Disparity in Unwanted Interventions. The relationship between unwanted interventions and autonomy in labor was further elucidated through a moderation analysis examining the role of maternal racial identity on mistreatment. This study found that, for people of color, the relationship between having an unwanted intervention and autonomy in labor is strengthened such that women of color have even less autonomy in decision making in their own labor, among patients who received unwanted interventions. Therefore, race composed a significant risk factor for increased mistreatment among those who had an unwanted intervention. This finding demonstrates that while unwanted interventions increase instances of mistreatment for all women, the harmful effects of this risk factor for mistreatment are even more pronounced for women who identify as Black, Asian, Latinx or Hispanic. Given this, the study concludes that racism and racial inequity compound the already detrimental effects of mistreatment risk factors, such as having an unwanted intervention. This experience was supported by interview participants, who described how Black women are offered more unwanted interventions in labor than their White counterparts. The findings illustrate how the decisions and choices of birthing people of color are more-often questioned by providers and that they endure more transgressions of their right to refuse treatment and face greater social pressure to acquiesce. Race, Mistreatment and Mortality Risk The amount of respect women received in maternity care significantly varied by race, and further, the effect of race on mistreatment was large. Women who identified as Caucasian and Asian had the highest level of respect in their maternity care, while those who identified as Black or Latinx endorsed only Moderate levels of respect, on average, and those who identified as - - 142 Hispanic endorsed Low respect, on average. Of import, no medical moderators were identified between race of the mother and mistreatment in childbirth. In fact, not even perception of racism by the mother moderated this relationship. This suggests that even if a mother does not perceive herself to be discriminated against, there is an objective relationship between her race and mistreatment such that those who do not identify as Caucasian are experiencing less respect in maternity care that is not explained by insurance status, high-risk pregnancy status, or even patient perception of provider racism. This provides evidence for a direct correlation between race and mistreatment. This finding refutes claims that higher rates of BMI>40, hypertension, and gestational diabetes account for the higher rates of mistreatment and mortality among Black women in the United States (Rosenberg et al., 2005). The study provides preliminary evidence for the existence and impact of systemic racism in maternity care within this sample and provides support for the study by Vedam et al which found that even when holding for other significant variables, there are meaningful differences in mistreatment by race (Vedam, 2019). There were several forms of mistreatment in the study which, in conjunction with the significant relationship between race and mistreatment, have implications for the high rate of maternal mortality among women of color in New York City (NYC Health Department, 2020). Prior studies have found that women of color, including Black, Hispanic and Indigenous women, are nearly three times as likely to experience mistreatment in labor in the form of being coerced into an intervention they do not want or their needs and concerns not being attended to in a timely manner (Vedam et al., 2019). These forms of mistreatment put women of color at increased risk for severe maternal mortality and preventable death related to childbirth due to medical neglect, hemorrhage, and untimely medical responses (Wynn, 2019). - - 143 The current study found that the forms of mistreatment which put women of color at increased risk for pregnancy-related death are occurring in New York City hospitals. Health concerns were dismissed by providers, participants experienced long periods of abandonment during active labor, and they endured medical neglect as well as racial discrimination during labor. These forms of mistreatment put women at-risk for serious and preventable medical complications in labor (Wynn, 2019; Vedam et al., 2019) As such, these findings suggest that racism in maternity care and the forms of mistreatment observed in this study may be contributing to the high severe maternal morbidity and mortality rates among women of color in New York City. Parity Finally, the study found that multiparous women experienced more respect in their maternity care than people giving birth for the first time. This echoes the findings of prior studies on mistreatment which found that multiparous women have less experiences of mistreatment (Vedam et al., 2019). This finding may be explained by the qualitative data, which illustrated that women who had a negative first birth experience often made efforts to change providers, give birth at a different hospital, change insurance providers or change childbirth method. These proactive behaviors seemed to increase their sense of autonomy and decrease the risk for mistreatment in subsequent births. Previous studies have found that primiparous women have lower self-efficacy and higher childbirth fear than multiparous women (Shakarami et al., 2021). As such, it seems that patients increased their own childbirth knowledge by the very nature of having experienced it once before and subsequently had higher self-efficacy and made different decisions and engaged in new behaviors, informed by their past experience. - - 144 Relationship between Maternal Mistreatment and Maternal Mental Health The present study identified a significant relationship between mistreatment in maternity care and maternal mental health. Specifically, the less respect and autonomy patients receive in their perinatal care, the more postpartum symptoms of depression and PTSD they experience. Importantly, those who experienced the least amount of autonomy in their labor and maternity care endorsed the most PTSD symptoms, indicating a significant risk factor of more severe PTSD with the most egregious transgressions of personal autonomy in childbirth. A recent study found that risk factors for PTSD related to recent childbirth (CB-PTSD) include frequent provider changes, no continuity of care during childbirth, poor emotional support from treatment team, and a lack of decision-making power (Kranenburg, Lambregtse-van den Berg, & Stramrood, 2023; Sobel et al., 2018). The qualitative results of the present study confirm this. The study found that women experienced frequent provider changes, poor continuity of care and disrespect of their decision-making power. These experiences were highly distressing for the participants. These findings help to further elucidate the significant correlation between PTSD and violations of autonomy and decision making in labor, identified by the study’s MADM measure. The relationship between mistreatment and mental health was illustrated by the following themes which emerged through the qualitative analysis: Abandonment and Confusion, Disregard for Patient Autonomy and Women being Treated as Passive. Interviewees felt confused, disappointed, and frustrated after their mistreatment experiences of abandonment, botched medical interventions and racial discrimination. While interview participants did not endorse current mental health symptoms, the three words used most to describe their childbirth experiences were “traumatic,” “scary” and “abandoned.” Many felt it was “unfair” they should feel this way during a vulnerable moment where they expected to feel empowered. Some blamed the treatment team - - 145 for feeling this way, others blamed themselves. These narratives illustrate the emotional experience of mistreatment from the perspective of the patients themselves. Together, these mixed-methods findings support the current research which has found that a loss of autonomy and feelings of “powerlessness” that occur in negative or traumatic birthing experiences are correlated with an increased risk for PTSD (Lukasse, Schroll, & Karro, 2015; Vedam et al., 2017; Beck, 2004). These findings powerfully demonstrate the relationship between the quality of interpersonal treatment in childbirth and postpartum mental health. As such, childbirth experience, not just childbirth outcomes, must be considered an integral determining factor in the maternal mental health crisis (MMHLA, 2023; Policy Center for Maternal Mental Health, 2023). Childhood Trauma, Childbirth Trauma, and Attachment. The study found that a history of childhood trauma constitutes a risk factor for mistreatment. Patients who experienced a significant number of adverse events in their early childhood, including mental illness in a parent, sexual violence or emotional neglect, were significantly more likely to endorse low levels of respect and autonomy in their maternity care. These observations reflect the findings of previous studies which have demonstrated the association between a history of trauma and the potentially re-traumatizing experience of childbirth (Wosu et al., 2015). Further, studies have shown that a history of sexual trauma and feelings of powerlessness in labor are together significant predictors of experiencing childbirth as traumatic (Soet, Brack & Dilirio, 2003). Along these lines, it has been established that higher ACE scores are correlated with a higher risk for major depressive disorder and PTSD in both the general population, and postpartum population (Chapman et al., 2004; Prentice et al., 2022). The study found that early childhood - - 146 trauma was significantly correlated with mistreatment in childbirth, even when holding for other variables such as a history of mental illness. ACE scores were also significant in predicting lowered autonomy in maternity care when examined in conjunction with other predictor variables of mistreatment in the path analysis, such as having a history of mental illness, and having an unwanted intervention. Further, the path analysis demonstrated that the indirect relationship between mental illness history and mistreatment influences postpartum mental health, but only in the context of prior trauma history. Together, these findings demonstrate the systemic and interdependent relationships between childhood experiences of trauma, history of mental illness, mistreatment in maternity care and postpartum mental illness. Finally, while the study did not find that mistreatment alone correlated with poor mother-infant bonding, mistreatment did negatively impact mother-infant bonding when the effect of childhood trauma was accounted for in this relationship. Studies have examined early childhood attachment experiences and their relationship to becoming a mother using the Adult Attachment Inventory (AAI). These studies provide evidence for the significant impact that early childhood attachment experiences have on mother-infant bonding (Thompson & Jaque, 2017; Murphy, Steele & Dube et al., 2013). The correlation between childhood trauma, mistreatment and mother-infant bonding observed in the study provides evidence that adverse childhood experiences may increase the risk for poor mother infant-bonding, when mistreatment in childbirth occurs. Together, these findings help to elucidate the relationship between childhood trauma, childbirth experience and subsequent postpartum mental health and attachment concerns. Systemic Influences on Maternal Mental Health Together, the results of the path analysis revealed the direct and indirect influences of race, childbirth preparation, history of mental illness and trauma history on the likelihood of - - 147 experiencing mistreatment in childbirth. The study also established the direct and indirect influences of these variables on the relationship between mistreatment in maternity care and postpartum mental illness. These significant interactions demonstrate how risk factors for mistreatment, including lack of childbirth preparation, birth plan or race of the birthing person, may compound risk factors for mistreatment within the systemic model. The path analysis also identifies protective factors that have the potential to moderate mistreatment. For example, a birth plan may mitigate the risk factor for mistreatment posed by a history of childhood trauma or one’s racial identity. As such, these findings demonstrate the existence of a systemic relationship between the race of the mother, how she is treated by her providers, and mental health correlates of this relationship as well as provide concrete recommendations for how to moderate the risk of mistreatment posed especially to women of color and trauma survivors. Provider Role in Preventing and Engendering Mistreatment Instances of mistreatment may occur at the individual-level of the provider and at the systemic level of the hospital. The study found that treatment teams appeared to have variable and inconsistent levels of training and approaches to maternal care, at both the hospital-level (i.e., frequent shift changes) and the individual-level (i.e., poor communication when shift changes occurred). As such, instances of mistreatment did not occur across all treatment providers but instead were isolated to single staff members or were the result of problematic hospital policies applied to all patients. Participants described reactions from providers to their colleagues’ neglectful, disrespectful or discriminatory behavior towards them in labor. They described an OB/GYN who was “outraged” at the behavior of her colleagues who were non-apologetic for serious medical - - 148 neglect, and another described an OB/GYN who became angry with the anesthesiologist team for their failure to listen to her patient’s pleas for pain relief when an epidural had been wrongly administered. This illustrates the inconsistency in care delivery, the disparity in provider attitudes towards patient autonomy, and the discrepant medical ethos within treatment teams (Francis, 2001). As such, this study found that inconsistency in training and discrepancies in the treatment team’s approach to care increased the risk of mistreatment and engendered negative mental health consequences for birthing people. Normalization of Mistreatment The current study found that the systemic forms of mistreatment observed in the study were at times normalized or accepted by both patients and providers. This may be due to the focus on “healthy outcomes” in labor instead of “healthy process.” The qualitative analysis identified how some survivors of mistreatment focused on things being “alright in the end” and embodied the sentiment that the “ends justify the means” which had the effect of accepting and normalizing these experiences. This study found that the administration of unwanted procedures was frequently normalized by patients and their providers, adding to the body of work identifying this as a common form of mistreatment in America (Bowser & Hill, 2010; Vedam et al., 2019). Researchers have attempted to capture this normalization of systematic forms of mistreatment under the umbrella term “unintentional harm,” whereby providers and patients do not recognize an intervention or interaction as abusive or coercive (Liese et al., 2021). This may include institutional constraints, such as frequent provider changes, or hospital policy, such as early induction or artificial rupture of membranes (Liese et al., 2021). The current study found instances of mistreatment that were normalized and may fall into this category of unintentional harm. These included long delays in care, “botched” epidural administrations, and failure to check - - 149 medical records before administering contraindicated medications. Despite the clear failure to provide adequate maternal care in these examples, these forms of mistreatment were often justified by patients who instead focused on their healthy infant outcomes. As such, the normalization of mistreatment which occurs at the level of the hospital and provider seems to have been internalized by the patients, thus lowering their expectations and increasing their acceptance of poor treatment. New York City Risk Factors: Birthing in a Different Borough The study observed an interesting discrepancy between the New York City borough of residence and the borough in which the patient gave birth. While approximately 70% of the sample resided in Brooklyn, 75% of the sample travelled to Manhattan to give birth. Only 18% of respondents gave birth at a hospital in Brooklyn. The qualitative sub-sample similarly followed this pattern, 100% of whom gave birth in Manhattan while only one participant lived there. The qualitative data illustrate how this discrepancy in boroughs created a problem for women regarding when to leave their homes and depart for the hospital. Arriving at the hospital “too early” was perceived to be correlated with an increased risk for unwanted interventions including Pitocin, artificial rupture of membranes, epidural and C-section. Most participants felt it was important to wait as long as possible to arrive to the hospital, due to the risk of being “on the clock” to progress in labor or face the “threat” of a Cesarean section. The statistical findings prove that these fears are not unfounded. Unwanted interventions and provider pressure were commonly endorsed by the sample and were associated with higher rates of mistreatment in maternity care. However, waiting too long to leave their home increased the stress of travelling to a different borough while in labor; participants discussed fears related to finding a parking spot, deciding whether to take an Uber or the Subway, and getting stuck in the U.N. General Assembly traffic. - - 150 Yet, despite the fear of arriving too early and the knowledge of risk associated with it, most of the sample still chose to travel up to 1.5 hours to give birth at a hospital in Manhattan; they often arrived in the early stages of labor and faced the very pressures they hoped to avoid. Women chose to travel to hospitals in Manhattan because they were perceived as “better” or were affiliated with their preferred OBGYN. While there is a high density of hospitals and a good ratio of hospitals to patients in each borough, patients chose to travel hours and in doing so potentially put themselves at-risk for some of the same sources of mistreatment faced by birthing people in rural regions where hospitals are far from home (Kozhimannil et al., 2016; Cromartie et al., 2020). As such, this observed phenomenon may present a unique risk factor for mistreatment among birthing women in New York City. This finding has implications for the study and collection of maternal mortality statistics in New York City. Recent data from the New York City Maternal Mortality Review Committee shows that the maternal mortality rate (MMR) varies significantly by “borough of residence” (NYC Open Data). Brooklyn has the highest MMR, with 33% of all pregnancy-related deaths in New York City occurring there. Manhattan has the lowest MMR, accounting for only 3% of maternal deaths (NYC Health Department, 2022). Given that many pregnancy-related deaths occur at the hospital and the data show that a large majority of women are traveling to hospitals outside of their home borough, these findings suggest that both birth borough and borough of residence should be captured when evaluating maternal mortality statistics. These findings suggest that as part of childbirth preparation, it would be advantageous for patients to consider the impact of traveling to a hospital that is outside of their borough, the risks associated with this, and their comfort-level with those risks. It also suggests that future research - - 151 is needed to clarify this relationship and capture the difference in mistreatment and birth outcomes by borough of birth in relation to travel distance from borough of residence. Main Findings in Context: American Medicine Right to Refuse Care and “Capacity” The study found that one quarter of the sample received an intervention to their body that they did not want. To understand the meaning of this statistic, a discussion of bodily autonomy and the right to refuse medical treatment must be explored. The right to refuse care is founded on the medical ethical principle of autonomy (Pirotte & Benson, 2023). This principle states that, “every person has the right to make informed decisions about their healthcare and that healthcare professionals should not impose their own beliefs or decisions upon their patients” (Pirotte & Benson, 2023). The “autonomy paradigm” dominates American bioethics in both medicine and psychology and is fundamental to the conceptualization of “health” and patient care. While it is steeped in and derived from American and Eurocentric values, it is nonetheless (and, arguably due to this cultural heritage) of great import to center autonomy as the antithesis to mistreatment in the findings of this work (Salles, 2002; Dive & Newson, 2018). In line with the medical ethical right to autonomy, the New York State Patient Bill of Rights states that patients have the right to, “refuse treatment and be told what effect this may have on your health” (Department of Health, January 2018). Patients who are found to have decision-making “capacity,” do not need a surrogate to make medical decisions on their behalf and have the right to refuse medical care Against Medical Advice (AMA) (AMA Code of Ethics, August 2023). The judgement of “capacity” within medical (not legal) settings is outlined by the Appelbaum criteria (Appelbaum & Grisso, 1988). A patient is found to have capacity if they can - - 152 form rational decisions, as assessed by their ability to understand the facts of the medical situation, appreciate the information, demonstrate reasoning, and clearly express a choice regarding their care (Leo, 1999; Appelbaum & Grisso, 1988). As with non-pregnant patients, pregnant patients have the right to refuse medical care in the case of childbirth if they have capacity (National Academies of Sciences, Engineering and Medicine, 2020). The stance of the American College of Obstetricians and Gynecologists is that “pregnancy is not an exception to the principle that a decisionally capable patient has the right to refuse treatment, even treatment needed to maintain life…a decisionally capable pregnant woman’s decision to refuse recommended medical or surgical interventions should be respected” (American College of Obstetricians and Gynecologists, 2016). When a patient is found to lack capacity for medical decision-making, it most often occurs in the context of intoxication, dementia or psychiatric illness, where it can be as high as 45% (Lepping, Stanly & Turner, 2015). In the case of decisional capacity in childbirth, it is estimated that only 2% of patients are found to lack capacity (Singh et al., 2021). This statistic suggests that it is unlikely that approximately 25% of the current sample were assessed for and found to lack the capacity for rational decision-making regarding their own medical care, that would render it necessary and ethical to administer an unwanted intervention in childbirth. Given this, the findings of this study suggest that patients are being offered or administered interventions they do not want at a higher rate than would be expected or necessary in this population, contrary to the recommendations of the American College of Obstetricians and Gynecologists, in violation of the legal and ethical obligations of providers and against the tenants of the New York State Patient Bill of Rights. - - 153 The high rate of unwanted interventions in the sample may be explained by the difficulty in exercising one’s right to refuse care. It is possible that patients are not informing their providers that the interventions are in fact unwanted, and this may be due to several reasons. When faced with a medical recommendation from their providers, 41% of the sample felt unable to say “no” and 25% of the sample reported that they were “pushed” by providers into accepting recommendations they did not want (See Appendix E and Appendix F for frequency tables of MORi and MADM item responses). Patients identified that provider attitudes created an environment that made it extremely difficult to refuse care. This finding is also illustrated by the qualitative data. A participant was told that if she did not receive Pitocin, which she did not want, that she could “bleed to death.” When she continued to refuse it, she was met with hostility before the treatment team made the same threat to her husband. This experience illustrates the interpersonal difficulty and social consequences of denying the recommendation of a treatment team for an unwanted intervention. Maternal Health and Women’s Rights: Reproductive Justice “Childbirth is not an emergency” (Wolf & Charles, 2018). A patient who has decisional capacity has the right to refuse “lifesaving” procedures, Against Medical Advice in labor (American College of Obstetricians & Gynecology, 2020; Wolf & Charles, 2018). The term “lifesaving” in maternity care often prioritizes the infant’s life over the mother’s life. This phenomenon may be explained in part by inaccurate and biased risk calculations informed by cultural norms: even a small risk to the fetus tends to outweigh substantial risks to the pregnant person (Lyerly et al., 2009). - - 154 Removal of Abortion rights, Removal of Birth Rights The American cultural practice of over-valuing infant well-being above maternal well-being is exacerbated by increasingly misogynistic and punitive abortion laws in the United States (Chadwick, 2018). The overturning of Roe v. Wade has set a dangerous precedent in valuing the rights of the infant over the rights of the mother (Paltrow, Harris & Marshall, 2022). The increasing challenge to the bodily autonomy of birthing people has enabled American courts to rule against the birthing person in cases of unwanted medical interventions in labor. This was the case in Dray v. Staten Island University Hospital and James J. Ducey whereby a hospital’s decision to operate on a “competent and conscious” patient against her refusal to consent was deemed permissible and upheld in subsequent appeals (Diaz-Tello, 2016; Brief for National Advocates for Pregnant Women as Amicus Curiae, 2016; Hon. Genine D. Edwards, 2023). In upholding the ruling, the court failed to condemn “obstetric violence” and mistreatment in childbirth in New York State Hospitals (Brief for National Advocates for Pregnant Women as Amicus Curiae, 2016; Bradley, 2017). This dangerous legal precedent ushers in an era that has begun to, “curtail fundamental rights for all those who become pregnant and will undermine their status as full persons meriting Constitutional protections” (Paltrow, Harris & Marshall, 2022). The 10-15% rate of mistreatment in the current sample in conjunction with the normalization of mistreatment in hospitals at the state-level together highlight the inextricable causes of birth activism and abortion activism which together posit every woman’s right to have complete control over her body (Shaw, 2013; Chadwick, 2018). The failure to prioritize the patient rights of women over fetal rights in American reproductive medicine is well-documented and comprises an inherent violation of personal autonomy (Shabot & Korem, 2018; Chadwick, 2018). The high rate of unwanted interventions - - 155 observed in this study contributes to this growing body of literature and ushers in a multidisciplinary ethical, medical and legal debate surrounding who the patient is, and, who gets to refuse care in birth. These findings provide further evidence for the tide turning away from woman-centered maternal care, which is the standard of care defined by the World Health Organization, and towards infant centered maternal care (Rovner, 2012; Frati et al., 2021; Weinman, 2022; Paltrow, Harris & Marshall, 2022; World Health Organization, 2018). Advocating for My Own Rights: A Double-Edged Sword Studies have shown that a woman’s right to full, informed and ongoing consent in labor is routinely ignored and consent is often given under provider pressure (Wolf & Charles, 2018). The present study found that self-advocacy aided patients in refusing care they did not want and resisting provider pressure for unwanted interventions. This was an unexpected result in the study and one the study did not set out to explore but nonetheless emerged as a theme throughout qualitative analysis. The study found that self-advocacy comprises a protective factor and a “defense” against mistreatment. Women who advocated for themselves did so by insisting on their right to refuse care and challenged providers who questioned their capacity for decision-making. However, self-advocacy as a prerequisite for a respectful birth reveals the implicit expectation that the patient is the only guard of their own care and safety. Self-advocacy in labor is an unreasonable and unfair expectation; systems of gender, power and race together make self-advocacy extremely challenging in medical settings, especially for perinatal populations. Self-advocacy is particularly difficult for marginalized groups including women, people of color and those with trauma histories (Hutchens, Frawley & Sullivan, 2023). Further, patient gender impacts the quality of patient-provider interactions and the patient’s sense of empowerment (Bertakis, Franks & Epstein, 2009). - - 156 Also, studies have found that trauma survivors may have more difficulty with self-advocacy and have more negative experiences with authority figures, such as medical care providers, which can lead to re-traumatization during childbirth (Rich & Garza, 2022). The findings of the qualitative analysis contribute evidence for this phenomenon. The study found that while some patients successfully advocated for themselves and the birth they desired, others felt unable to challenge or speak up to providers and these individuals experienced more negative birth outcomes and mental health effects. Given that those who have trouble advocating for themselves in medical settings are those who already possess risk factors for mistreatment, this finding suggests that self-advocacy may only be accessible to certain populations. This may further compound mistreatment risk factors for women of color, trauma survivors and marginalized groups. As such, it is a moral and legal imperative that treatment teams create an environment where a patient’s right to refusal can be fully exercised and where providers are the primary advocates for patient respect and autonomy. A forced consent is not full and informed consent; birthing patients should never be put in the unfair position of defending something to which they have an inherit right (National Academies of Sciences, Engineering and Medicine, 2020). Barriers to Improving Hospital Births in America The findings of this study reflect birth experiences in hospitals, where 98.4% of people give birth in America (National Academies of Sciences, Engineering and Medicine, 2020). Studies have established that the risk of unwanted intervention, unnecessary C-section and rates of mistreatment are lower at planned home births and birthing centers as compared to hospital births, among low-risk births (Souter et al., 2019). While insurance providers must provide coverage for midwifery care in planned homebirths in New York State, less than 2% of birthing - - 157 people choose this route for a variety of reasons including health complications, limited access, higher cost, desire for pain management, fear of childbirth and preference for surgical interventions in birth. Surgical birth can provide life-saving procedures however it is not without risks which must be explained to patients so they can give full and informed consent (Hansen et al., 2013). The risk of unnecessary surgical interventions is higher in hospital births (Jansen et al., 2013; National Academies of Sciences, Engineering and Medicine, 2020). The “cascade effect” of the medicalization of labor in hospital births is well-documented, whereby unnecessary medical interventions meant to reduce risk actually increase risk for birthing people (Bertran et al., 2016; Jansen et al., 2013). This “too much too soon” medical approach in the United States places women at increased risk for severe maternal morbidity (SMM), mortality and postpartum mental illness (Miller et al., 2016; Dekel et al, 2019). The results of this study suggest that surgical birth in hospitals engenders opportunities for mistreatment. Women are often not prepared for the risks associated with hospital births, providers are not providing opportunities for informed consent and refusal, and the right to autonomous decision-making around birth interventions is frequently challenged. The occurrence of mistreatment and postpartum mental illness identified in the present study occur in the cultural context of the United States. There are great systemic shifts that would need to occur to reduce the rates of mortality, SMM and postpartum mental illness in the United States (Tikkanen, 2020; WHO, 2023). Some of these changes include: healthcare coverage for postpartum visits and home visits from nurses, increased access to midwifery care, the first postpartum OB/GYN visit occurring earlier than 6-weeks postpartum, community-level education and intervention, and decreased Cesarean section rate; many of these healthcare initiatives have - - 158 been credited for the low mortality rates in the United Kingdom, Canada and Australia, the absence of which in the United States provide insight into the mechanisms for our growing maternal mortality rate (Collier & Molina, 2019; WHO, 2023). The cultural context which would make these changes difficult include the profit-driven industry of medicine, medicalization of childbirth in the United States, systemic racism which courses through all aspects of American society leaving inequity in its wake, conservative cultural attitudes towards birth, policy and law which de-value the autonomy of women, and the power of patients to sue for medical malpractice in the United States leading to conservative medical practice and over-intervention (Shabot & Korem, 2018; Chadwick, 2018; Athan, 2020; Barber, 2011; Lyu et al., 2017; National Academies of Sciences, Engineering, and Medicine, 2020; Johnson, September 2010). This cultural context provides the atmosphere for preventable maternal deaths to rise in the United States even while they fall in comparable nations; it also helps to explain the increase in postpartum depression and maternal mental health risk factors and provides the backdrop for the significant findings of the present study (Tikkanen, 2020; Policy Center for Maternal Mental Health, 2023). The current study provides increased insight into the forms of mistreatment that can render childbirth dangerous for American women and illustrates the stories of survivors of mistreatment within a broken, complex, medical birthing system. Clinical Implications It is known that postpartum mental illness is a multi-determined phenomenon which includes interpersonal, psychological, biological and social determinants of health (O’Hara, 2009; Ross et al., 2004). The findings of the present study demonstrate that mistreatment in childbirth is - - 159 a likewise multidetermined phenomenon, which is interdependent with mental health. This interdependence is demonstrated by the significant findings of the path analysis which illustrates the direct and indirect effects of childbirth preparation, race and early childhood adversity on mistreatment in maternity care and subsequent postpartum mental illness. As such, clinical work with postpartum people must address these interdependent relationships. Mental Health Practitioners Maternal mental health cannot be viewed in a vacuum. Becoming a mother through birth necessarily implicates birth experience. Given the high concordance between birth preparation, mistreatment and postpartum mental health observed in the study, it is recommended that the creation of birth plans and psychoeducation regarding patient rights in hospital births be integrated into therapy treatment with perinatal patients (NYC Health Department, 2023). Currently it is recommended clinical practice to explore lifetime sexual and reproductive history in therapy with perinatal populations, especially among psychiatric populations (Genna et al., 2012). Further, the therapeutic value of sharing birth narratives and exploring the “reproductive identities” of our perinatal patients has been established in the literature (Farley & Widmann, 2001; Athan, 2020). Building upon this work, the findings of this study suggest that mental health practitioners should assess childbirth history with their perinatal patients and administer the Mothers on Respect Index (MORi) and Mother’s Autonomy in Decision Making Scale (MADM) to assess history of mistreatment in maternity care and to identify patients who may be at risk for postpartum mental illness as a result of mistreatment (Vedam et al., 2017; Vedam et al., 2017). Treatment goals should be formulated in relation to these measures of mistreatment and aimed at exploring how negative childbirth experiences and forms of mistreatment contribute to postpartum suffering and interact with patient history. - - 160 Addressing Psychological Symptoms of Mistreatment The appropriate mental health treatment approach for mistreatment depends upon symptomology and diagnosis. For PTSD related to childbirth, Cognitive Processing Therapy is recommended (Gobin, Boyd & Green, 2023). For postpartum depression and anxiety, Cognitive Behavioral Therapy, Interpersonal Psychotherapy, and attachment-focused psychodynamic psychotherapies are effective options (Stewart & Vigod, 2019; Valverde et al., 2023; Huang et al., 2020). As always, any treatment approach with perinatal populations should be symptom-specific, trauma-informed, feasible, acceptable and appropriate. Medical Practitioners and Health Services The results of the study identify areas for improvement at two levels of maternity care to mitigate mistreatment risk: at the level of the patient and at the level of the provider. At the level of the provider, there are several approaches to the practice of maternity care that address the power differential in the birthing room and work to collaborate with patients to center respect and dignity at the heart of medical care. These include patient-centered approaches, Woman-Centered Care and Trauma-Informed Care (TIC); these evidence-based methods of practicing medicine identify the patient at the center of care, rather than the infant or the treatment team (Davis et al., 2021; Bertakis, Franks & Epstein, 2009; Shields & Candib, 2023; Kranenburg, Lambregtse-van den Berg, & Stramrood, 2023; Brown et al., 2021). Trauma-Informed Care Trauma-informed care (TIC) is the recommended approach to working with birthing people who have a history of trauma. TIC creates a supportive and respectful environment that places the patient firmly in control of her care, ensures patients have full power over decisions and procedures, and enables self-advocacy without barriers (Kranenburg, Lambregtse-van den Berg, - - 161 & Stramrood, 2023). This approach to the practice of maternity care has been shown to decrease PTSD symptoms related to childbirth and reduce the risk of re-traumatization during childbirth following a history of sexual trauma (Bertakis, Franks & Epstein, 2009; Shields & Candib, 2023; Kranenburg, Lambregtse-van den Berg, & Stramrood, 2023). Based on the significant correlation between childhood trauma history and mistreatment in maternity care observed in the present study, TIC is an evidence-based recommendation that should be integrated into medical practice to prevent mistreatment and mitigate maternal mental health risks. Woman-Centered Care Another appropriate treatment approach is Woman-Centered Care, which is a marker of quality maternity care globally and is the recommended gold-standard for maternity care by the World Health Organization (2018). Woman-centered care is a common approach in midwifery care and it can be easily integrated into hospital care by OB/GYNs (Davis et al., 2021). This approach focuses on each woman’s unique needs as defined by herself. It respects her right to choice, autonomy, control and self-determination; it addresses her cultural, psychological, spiritual and physical needs, and does not focus on the needs of the health service system or provider (Davis et al., 2021; Shields & Candib, 2023; World Health Organization, 2018). This study found that violations of autonomy were common forms of mistreatment in the sample as well as provider failure to listen to the needs of their patients. These findings suggest that a Woman-Centered Care approach would be effective at preventing these forms of mistreatment. A helpful feature of this approach is that it requires the provider to collaborate with the patient, because effective delivery of Woman-Centered Care depends on the quality of the patient-provider relationship (Fontein-Kuipers, De Groot, & Van Staa, 2018). Therefore, this - - 162 approach would help to address not only the forms of mistreatment but would work to improve the quality of collaboration and patient-provider relationship. Collaborating on with Patients and Fellow Providers To improve care delivery at the level of the patient, the study identified an important relationship between birth preparation and childbirth experience. It is therefore recommended that medical practitioners integrate birth planning and childbirth education into their practice with prenatal women, if not doing so already. This should be a balance of listening and educating. It is necessary to discuss possible interventions that could occur in labor, to communicate openly about the patient’s expectations, and to document the patient’s preferences. It is also recommended that all primary providers educate patients on their rights in childbirth and provide information to the patient on the choices they will and will not have in a hospital birth. The New York City Health Department has created a helpful guide for birthing people, which helps them make a birth plan, provides education on their rights, explores insurance and cost information for hospital births, and discusses possible interventions that can occur in labor. It is recommended that medical professionals who treat pregnant people provide all patients with this “Companion Guide to the New York City Standards for Respectful Care at Birth” and utilize it as a tool when speaking with patients to prepare them for childbirth and to build a collaborative patient-provider relationship (NYC Health Department, 2023). To prevent mistreatment and unwanted interventions, providers and hospitals must obtain full, informed and ongoing consent from their birthing patients and respect their right to refuse treatments. This process of consent should be seen as an opportunity for collaboration between the patient as well as for the treatment team. To support providers, it is recommended that health systems engage in collaborative care practices to encourage and assist individual providers in - - 163 offering safe alternative options for care when requested (National Academies of Sciences, Engineering, and Medicine, 2020; Jansen et al., 2013). Routine Measurement of Mistreatment New York City has implemented the U.S. Preventative Services Task Force’s recommendation that all pregnant and postpartum women be screened in primary care for perinatal depression (Department of Health, 2022). The present study recommends that mistreatment in childbirth should also be routinely measured and discussed at the postpartum visit. The Mothers on Respect Index (MORi) and Mother’s Autonomy in Decision Making Scale (MADM) are quick, user-friendly, self-report scales that could be added to the mental health screeners already provided to patients at primary care visits (Vedam et al., 2017). The routine measurement of mistreatment would provide an opportunity to detect any potential postpartum mental health complications as a result of mistreatment and provide real-time feedback to providers and opportunities for improvement. Further, the MORi and MADM may be integrated into prenatal healthcare visits as a preventative measure for mistreatment. Given that the measures assess respect and autonomy in decision-making across maternity care, it is possible that the incorporation of mistreatment measures into prenatal care may allow both providers and patients to explore and prevent concerns that might otherwise only arise in the birthing room, such as differences in approach to care and disagreements regarding the patient role in her own care. Further, self-report measures can also serve as an educational tool. The preventative use of MORi and MADM affords patients the opportunity to learn about their rights and options in maternity care, to reflect on their perception of the quality of care they have received thus far with their current treatment team, and to change providers if desired. - - 164 Anti-Racist Maternity Care Finally, the study identified a pattern of racism and discrimination in maternity care, which reflects the larger trend of maternal health disparities by race in New York City. It is a moral, ethical and legal imperative for hospital systems to examine how systemic oppression and racism operate and are maintained in the birthing room. It is recommended that New York City hospitals work to implement anti-racist interventions in healthcare settings (Hassen et al., 2021). Focusing on addressing structural racism provides actionable avenues for changing patterns of practice that reinforce discriminatory beliefs and perpetuate harmful practices (Bailey et al., 2017). This would require leadership buy-in, training of providers, the implementation of transparent accountability mechanisms, increased representation and opportunities for medical providers of color, ongoing education and self-assessment (Hassen et al., 2021; Nelson, Prasad & Hackman, 2015). Just as consent and respect are ongoing processes that must be constantly earned, anti-racist approaches to birth must be constant, self-reflective and deconstructionist to address structural inequities. Research Implications The findings of the present study identify several leverage points to address and to continue to explore the risk factors and potential drivers of mistreatment in New York City hospitals. First, the relationship between birth plans and unwanted interventions in labor should be further explored and elucidated. There is a need for a systematic study of birth plans, including defining them and exploring the mechanism(s) that render birth plans effective in moderating mistreatment. Some hypotheses of this mechanism include: the research and education required to make a birth plan, the process of communicating with the provider required to share the birth plan, the process of receiving help and support from other holders of knowledge such as doulas, childbirth educators, - - 165 partners or fellow birthers, the self-selection of those who choose to make a birth plan, or the self-selection of providers who encourage birth plans. Any of these may contribute to patients being more informed, more self-confident, and empowered to make choices in line with their needs, desires and expectations. A second point of future research is to further define, quantify and measure unwanted interventions in childbirth, as it was used in the present study. To understand the frequency and forms of this phenomenon in hospital birth, it is necessary to validate an objective measure of unwanted interventions so that there can be a shared understanding of what this means to both providers and patients. A meta-analysis exploring previous studies on unwanted interventions in the United States would also be beneficial in clarifying the construct so that future research builds upon current knowledge, addresses a valid and reliable measure of this construct, and so that interventions aimed at reducing unwanted interventions are evidence-based. Finally, there is a need for increased interdisciplinary research between maternal health practitioners and mental health professionals via the systematic use of comparable, validated instruments to measure mistreatment and mental health outcomes. The diversity of typologies and definitions of ‘mistreatment in maternity care’ used across the fields of clinical psychology (i.e., “traumatic childbirth”), public health literature (i.e., “disrespect and abuse in labor”) and medical literature (i.e., “obstetric iatrogenesis”) pose a barrier to integrating findings and implementing solutions to help the same population. The great and important diversity of thought surrounding mistreatment in maternity care may be maintained through diverse treatment approaches, implementation strategies, research methods and unique academic lenses, but the results of these efforts need to be directly comparable. Utilizing the same measure of mistreatment would enable - - 166 diverse researchers to ensure they are measuring the same construct thus enabling this body of work to grow and be generative. It is therefore recommended that future studies utilize the MORi and MADM scales to build upon the current study and identify leverage points in the birthing system (Vedam et al., 2017). These scales are currently the most well-studied and validated instrument to measure mistreatment in this population in the United States and therefore present an opportunity for bringing together the diverse field of maternal research to address the same goal. Limitations and Future Directions Recruitment Method The findings of the present study must be interpreted considering several limitations. First, while attempts to recruit participants at a variety of in-person sites including hospitals, women’s shelters, daycares and pediatrician’s offices were made, no responses came from in-person recruitment. This limits the sample to those who are members of online groups, social media or who receive email listservs that are known to the researcher. Further, a significant number participants were recruited through online advertisement on a members-only parenting website in Brooklyn. While this gave the responses greater validity and lessened the chances of fraudulent responding, it also limited the diversity of recruitment from that site to those who lived in the borough of Brooklyn and who could afford to pay for the website membership. Representation Most of the sample identify as Caucasian and live in middle to high-income households. Therefore, findings must be interpreted considering the greater privilege afforded to these participants and the dearth of racial and socio-economic representation in the sample. Even so, - - 167 there was a significant correlation between mistreatment and race observed in the study and a 10-15% rate of mistreatment overall in the sample. The fact that these data came from a majority-Caucasian sample provides support that the phenomenon is likely to be compounded in a more racially diverse sample. A further limitation is that the study solely encompasses the experiences of cis-gendered women who identify as women. Further, while the study included a measure of mistreatment due to sexual orientation and/or gender-identity, the study did not collect data on each participants’ sexual orientation. This is a limitation of the study as it fails to capture the full scope of diverse birthing experiences in New York City and does not represent the narratives of individuals who experience mistreatment due to diverse sexual or gender identities. Online Data Collection All health data that is collected online is subject to the threat of fraudulent responses. Therefore, validity is always a concern with online studies. The present study implemented a thorough, evidence-based and dynamic validation process of all online responses to address this threat to validity. The study found that the Re-captcha bot-detection method was not sufficient in identifying fraudulent responses from bot-farms or from fraudulent individual respondents. Free-form response styles for open-ended questions and questions which require numbers or measurement words, reliability assessments built into each respondent’s survey session as well as logic questions together were effective at exposing fraudulent responses. As a result of this system, the present study asserts that the responses included in the analysis were valid and an accurate representation of the purported sample. - - 168 COVID-19 Finally, these data were collected from a cohort of people who gave birth following the COVID-19 pandemic. All participants in the current study gave birth between September 2021 and December 2023, during which time coronavirus was generally in decline in New York City (NYC Health, 2023). Only 2.7% of participants were COVID-positive during their births (n = 3). While mask mandates were still in effect for visitors during this time and some hospitals required COVID testing, the restrictive measure banning support people from the delivery room ended in New York City via executive order on March 28, 2020, before the present sample had given birth (Mayopoulos et al., 2021; Van Syckle & Caron, March 2020). Even so, considerable differences in birth experience may persist for years following the pandemic in ways not captured by the measures of the current study. Given this, any comparisons made between mistreatment statistics collected by the current study and those collected prior to 2020 should consider this history effect. Future Directions Future research on this subject matter should utilize purposive sampling to ensure that more diverse birthing populations are included in research on mistreatment, including transwomen, gender-nonconforming birthing people, Black, Latinx and Asian birthing people and members of undocumented communities. These communities necessitate inclusion in this research due their historical underrepresentation in health research and due to the higher risk of mistreatment posed to marginalized groups. Future research might also utilize statistical methods such as bootstrapping or weighting of the sample (or, survey weighting) in order to extrapolate results in a representative sample (Davison & Hickley, 1997; Pfefferman, 1996). To capture the provider perspective on mistreatment in New York City, future studies should conduct stakeholder interviews with medical providers, nurses, and hospital administration - - 169 to explore drivers, risk factors and forms of mistreatment from the perspective of providers, in the style of Salter et al. (2023). This would help to identify leverage points for change and increase buy-in at the level of the provider and hospital. Finally, the present study should be scaled-up to examine mistreatment using a longitudinal and sequential design. This would enable researchers to explore mediations between the variables of interest, and to identify the effect of birth plans on mistreatment and mental health outcomes. - - 170 CHAPTER VII: CONCLUSIONS The goal of this study was to explore mistreatment in maternity care and childbirth in New York City hospitals and to understand its relationship with maternal mental health. Mistreatment was defined by Bohren and colleagues seven “dimensions of mistreatment” in maternity care (2015). To do this, the study utilized a mixed-methods cross-sectional approach to examine the prevalence of this phenomenon, the potential drivers of mistreatment in maternity care, as well as any risk and protective factors. In total, 109 participants completed an anonymous online survey comprising a 41-item self-report measure which assessed: childbirth information, health history, mental health history, sociodemographic and economic data. Participants also completed several mental health questionnaires including the PHQ-9, the PCL-5, the ACE, the MIBS and the PRS, as well as two measures of mistreatment in maternity care, the MORi and the MADM. Following this, 8 participants were interviewed about their childbirth experience to capture the first-hand experience of mistreatment in maternity care. The quantitative data was statistically analyzed utilizing linear regression, moderation analysis and structural equation modeling, and the qualitative data was thematically coded then analyzed using Reflexive Thematic (RT) analysis. The mixed-methods data were triangulated to synthesize the results of the path analysis with the final qualitative themes to produce a comprehensive “model of mistreatment” to illustrate the direct and indirect relationships between mistreatment and maternal mental health in this sample. The study found that 10-15% of the sample experienced mistreatment in the form of Low to Very Low respect and/or autonomy in decision making in their maternity care. These findings suggest that on average, birthing people in this sample experienced similar levels of mistreatment - - 171 in New York City hospitals as compared to the 17% rate of mistreatment in the greater United States, as measured by the MORi and MADM scales (Bowser & Hill, 2010; Vedam et al., 2019; Diaz-Tello, 2016). Forms of mistreatment identified in the sample included the administration of unwanted procedures, providing procedures without offering alternate options or time to consider options, patients and their partners being pressured by providers to undergo procedures, verbal threats, dismissal of women’s concerns about their health, racial discrimination, abandonment, medical neglect and poor pain management. Together these forms of mistreatment represent several of the categories of mistreatment identified the World Health Organization and by prior studies of mistreatment in the United States (Bohren, 2015; World Health Organization, 2015; Vedam et al., 2019; Morton et al., 2018; Salter et al., 2023). The most significant predictor of mistreatment in the current study was having an unwanted intervention in childbirth; approximately 25% of respondents indicated that they received an intervention that they did not want during their labor. The study found that participants frequently reported being “pressured” by providers into accepting procedures they did not want or had already said “no” to. Participants also reported feeling unable to contradict the recommendations of providers and some felt they had to “fight” to avoid unwanted medical interventions. The study examined moderators of the relationship between unwanted interventions and mistreatment and found that not having a birth plan increased rates of mistreatment among those who had an unwanted intervention. This suggests that while unwanted interventions negatively impacted autonomy in maternity care, patient autonomy was even lower among those who did not have any form of birth plan. Further, the study found that having a birth plan may function to increase one’s perception of autonomy being removed when unwanted interventions are - - 172 administered. As such, the study found that birth plans increase one’s awareness of autonomy and actual autonomy in labor, which can have a mixed impact on mistreatment experiences. Finally, among patients who had an unwanted intervention, non-Caucasian patients had less autonomy and decision-making power in their own labor when compared to their Caucasian counterparts who also received an unwanted intervention. Thus, while unwanted interventions increased instances of mistreatment for all women, this risk increased even more so among women who identify as Black, Asian, Latinx or Hispanic. This finding illustrates how racism and racial inequity compound the already detrimental effects of mistreatment risk factors, such as having an unwanted intervention. The amount of respect women received in maternity care varied significantly by their racial identity; Black, Latinx and Hispanic women experienced the lowest levels of respect from their treatment teams as compared to their Caucasian and Asian counterparts. This effect was not explained by or moderated by medical complications, poor maternal health, insurance type, or even the perception of racism and discrimination by the patient from her providers. The study therefore identified a direct and objective correlation between race and mistreatment. With respect to maternal mental health and mistreatment, the study found that mistreatment in maternity care is correlated with postpartum depression and PTSD. This finding builds upon prior studies which found that a loss of autonomy in birth is associated with postpartum mental illness (Lukasse, Schroll, & Karro, 2015; Vedam et al., 2017; Beck, 2004). Participants who experienced mistreatment in the current study described the mental health effects of their experience; they felt confused, disappointed, frustrated and discriminated against during their births. With respect to mental health risk factors of mistreatment, the study demonstrated a relationship between early childhood trauma and childbirth experience, such that patients who - - 173 experienced a significant number of adverse events in their early childhood were more likely to endorse low levels of respect and autonomy in their maternity care. The findings of this study have implications for medical and clinical practice with perinatal populations. Patient-centered, Woman-Centered and Trauma-Informed approaches to maternity care place the power back in the hands of the patient. These approaches to the practice of maternity care have been shown to decrease postpartum trauma symptoms in relation to childbirth (Bertakis, Franks & Epstein, 2009; Shields & Candib, 2023; Kranenburg, Lambregtse-van den Berg, & Stramrood, 2023). The findings of the current study provide support for these approaches. The study also identified a relationship between birth preparation and childbirth experience. It is therefore recommended that medical practitioners integrate birth planning and childbirth education into their practice with prenatal women to discuss possible interventions in labor, to communicate openly about expectations, to document patient preferences, to educate the patient on her rights and to discuss what is feasible and possible in a hospital birth. With respect to mental health workers, the findings of the study suggest that mental health practitioners should assess childbirth history with their perinatal patients and administer measures of mistreatment as part of intake evaluations to identify patients at risk for postpartum mental illness. It is recommended that mental health workers treat the psychological sequalae of mistreatment with the appropriate treatment method for the presentation, diagnosis and context, with consideration for how the form of mistreatment (i.e., loss of autonomy) contributes to the psychological presentation. These might include Cognitive Processing Therapy (CPT), Cognitive Behavioral Therapy (CBT), Interpersonal Psychotherapy (IPT), and attachment-based psychodynamic approaches. - - 174 Finally, the study identified a pattern of racism and discrimination in maternity care. These findings challenge all providers to take responsibility for providing equitable, respectful, and dignified care to women despite a system which engenders the opposite. It is therefore necessary for hospitals to examine how systems of oppression and racism exist and are maintained in the birthing room. A Call to Action Just as every human has the inherent right to dignity, respect and autonomy, so too does every birthing person. Birth can be an opportunity for empowerment, self-determination and self-efficacy. This study is a reminder of the strength of birthing people who put their trust in their providers to have their best interests in mind during their most vulnerable moments. “The way a culture treats women in birth is a good indicator of how well women and their contributions to society are valued and honored;” it is therefore paramount that the fundamental human rights to bodily-autonomy and respect inherent in women, birthing people and those living with mental illness are centered in the debate on maternal health in the United States (Gaskin, 2011). The purpose of this study was to continue the tireless work towards the reduction of avertible maternal deaths in New York City and to end unnecessary mental anguish surrounding childbearing. It is the hope of this author that these results will comprise a “call to action” towards the reduction of preventable suffering among the birthing people of New York City. - - 175 APPENDIX A Research Questions for Qualitative Aims (Aim 5.) Organized by Domain Birth stories & narratives patient birth stories and narratives How do women tell their birth stories (i.e. affect, expression, meaning-making, details shared and left out). How do women make sense of their experiences in labor? Preparation, knowledge, and expectations before the childbirth How did women prepare for their labor and did they feel prepared? What did they not know, what do they wish they had known? What were women’s expectations of their birth experience? How do they describe their actual birth experience? How do women understand/explain any discrepancies? What is the degree of women’s literacy with labor/childbirth and postpartum education? To what extent are women educated/understanding of birth/labor processes, insurance, hospital systems, biology, etc.? Factors which participants believe influenced the course, experience & outcomes of their birth What factors do mothers perceive to have impacted their birth outcomes and their experience of their birth (mistreatment)? How do women understand the medical decisions made during labor, and the decision-making power in labor? Are there any personal, social or environmental factors that the mothers believe placed them at risk for any mental or physical health outcomes? How do they understand this? Perceptions of care & treatment How did women perceive the perinatal care they received? How do they perceive their treatment team and other care providers? How were disagreements handled, if any? How do women understand their mental health before and after labor? Any changes? How are these explained by them? - - 176 Perceived emotional and psychological impacts of their birth experience How do women feel their birth experience impacted them emotionally or psychologically or spiritually? How do they understand this impact? How do women make sense of experiences of mistreatment in labor? - - 177 APPENDIX B Online Self-Report Survey Questions Organized by Domain and Survey Response Format (18) Survey Questions to Capture the Domain of Childbirth Information Survey Response Format 1) What was the date of your most recent childbirth (the “index pregnancy”)? 2) Is this your first childbirth? 2a) If not, please indicate how many births you have had. 3) Did you have a birth plan (i.e. plan for how you wanted your birth to go, what procedures you did and did not want)? -Did you share your birth plan with your healthcare provider? -If so, was your birth plan followed by your healthcare team? -Did you have a C-section? -If so, was it a planned C-section? -Was it an emergency C-section? -Did you attend a childbirth class before this birth? -Have you ever attended a childbirth class? -If you had a vaginal birth, did you receive an epidural? -Did you have any interventions in labor that you did not want? -Were you induced (given Pitocin to bring on labor)? -Did you receive an episiotomy? -Did you give birth in a hospital? -Did you have a partner, family member, doula, or friend accompany you throughout your birth? -Were you COVID-19 positive during your childbirth? -Did your infant spend time in the NICU? -If so, did they spend more than one week in the NICU? -Did you experience hemorrhage or excessive bleeding following birth? -Please indicate the weight of your infant at birth. -Please check the box of the borough in which you gave birth. - Dropdown menu -Yes or No -Dropdown menu -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Dropdown menu -Dropdown menu (5) Survey Questions to Capture the Domain of Medical/Health History Survey Response Format -Do you have health insurance? -If so, do you have private health insurance? -If so, do you have Medicaid? -Did you receive prenatal care from a physician, nurse or midwife during your pregnancy? -Were you diagnosed with gestational diabetes? -Did you have hypertension during your pregnancy? -Were you considered by your doctor to be “overweight,” “obese” or to have a higher-than-average BMI during your pregnancy (BMI that is 30 or greater)? -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No (12) Survey Questions to Capture the Domain of Sociodemographic Information Survey Response Format - - 178 -In which borough do you reside? -What is your zipcode? -What is your race/racial identity? (Black, Latinx, Hispanic, Caucasian, Asian, Middle-Eastern, Other) -Did you complete 12 years of education? (i.e. Did you graduate high-school or obtain a GED)? -Did you complete any higher education (i.e. associates degree, bachelor, master)? -Are you currently employed (i.e. on paid maternity leave or working currently)? -Please indicate your household income. -What is your age? -Do you have a partner or spouse? -Do you receive any financial assistance (i.e. social security benefits, disability benefits, unemployment benefits, COVID financial assistance)? -Are you an American citizen? -Please indicate religious affiliation, if any. (Agnostic, Atheist, Buddhist, Catholic, Christian, Hindu, Jewish (reform), Jewish (Orthodox), Muslim, Sikh, Spiritual, Other). -Dropdown menu -Dropdown menu -Dropdown menu -Yes or No -Yes or No -Yes or No -Dropdown menu -Dropdown menu -Yes or No -Yes or No -Yes or No -Yes or No -Dropdown menu (6) Survey Questions to Capture the Domain of Mental Health History Survey Response Format -Have you ever had depression, anxiety or any other form of mental illness? -Have you ever considered suicide or attempted suicide? -Have you ever been in therapy (i.e. with a mental health counselor, therapist or psychologist)? -Have you ever taken prescribed medication for mental illness? -Have you experienced intimate partner violence in your life (i.e. emotional or physical abuse in a close relationship)? -Have you experienced sexual violence or sexual assault in your life? -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No -Yes or No - - 179 APPENDIX C Recruitment flyer used during data collection to advertise the study. Visit: shorturl.at/nrNS1Or, scan the QR code: WHAT WILL I BE ASKED TO DO?Complete a 45-60 min online survey. Eligibleparticipants who complete the survey will be paid$15. Responses will remain confidential. PURPOSE OF THE STUDYWe seek to understand how women perceive thecare they receive in childbirth from their healthcareproviders, and the impact of this on health andmental health outcomes!DO I QUALIFY?If you are an adult who has given birth in an NYChospital in any of the 5 boroughs in the last year,you may be eligible!TO PARTICIPATE: Potential risks of participation include emotionaldiscomfort and boredom. Potential benefits includethe opportunity to share your childbirth experience.STUDY INFORMATIONThis study was approved by the Teachers College IRB (ID: 22-235). Questions? Email the PI: Anika Alix at childbirthnyc@tc.columbia.eduSTUDY ONCHILDBIRTHEXPERIENCE IN NYC!RECRUITING PARTICIPANTS FOR A...- - 180 APPENDIX D Spearman Correlations of MORi and MADM with Mental Health Variables Spearman Correlation Matrix Among MIBS_Total, MADM_Total, MORI_Total, Caucasian_Scale, PR_Total, PCL5_Total, ACE_Total, PHQ_Total, and MI_Hx_Scale Variable 1 2 3 4 5 6 7 8 9 1. MIBS_Total - 2. MADM_Total .10 - 3. MORI_Total -.08 .63* - 4. Race Scale -.17 -.03 .13 - 5. PR_Total .07 -.10 -.02 .14 - 6. PCL5_Total .14 -.18 -.49* -.10 .06 - 7. ACE_Total -.02 -.24 -.19 -.09 .16 .20 - 8. PHQ_Total .18 -.01 -.23* -.27 .02 .68* .20 - 9. Mental Illness Hx .10 -.06 -.05 .19 .26 .09 .28 .08 - Note. *p <.05. p-values adjusted using the Holm correction. The result of the correlation was examined based on an alpha value of .05. Spearman Correlations of MORi and MADM with Birth Variables Spearman Correlation Matrix Among Total_Births, MORI_Total, MADM_Total, Unwanted_Interventions_Scale, NICU_Scale, Induced_Scale, Diabetes_Scale, Overweight_Scale, Hypertension_Scale, Caucasian_Scale, C_section_Emergency_Scale, and Childbirth_Class_Lifetime_Scale (Scale variables were made into Dummy variables) Variable 1 2 3 4 5 6 7 8 9 10 11 12 1. Total_Births - 2. MORI_Total .23* - 3. MADM_Total .21 .59* - 4. Unwanted Interventions -.39 -.41* -.33* - 5. NICU -.16 -.11 .01 .04 - 6. Induced -.33 -.14 -.30 .30 -.42 - 7. Diabetes .03 -.29 -.27 .09 -.20 .04 - 8. Overweight .09 .15 -.06 -.08 -.13 -.02 .28 - 9. Hypertension -.14 -.12 -.20 .22 .51 -.00 -.19 -.26 - 10. Caucasian -.37 .04 .01 -.01 -.28 .20 -.03 .08 -.17 - 11. Csection Emergency -.28 -.19 .07 .39 -.12 .46 -.10 -.11 .06 .15 - 12. Childbirth Class -.24 -.22* -.24 .25 -.28 .22 .22 .00 -.05 -.04 .16 - Note. *p <.05. p-values adjusted using the Holm correction. The result of the correlation was examined based on an alpha value of .05. - - 181 APPENDIX E Frequency Table for Mother’s Autonomy in Decision Makin Scale (MADM), by Item Frequency Table for MADM Responses by Item Item Question n % “My doctor or midwife asked me how involved in decision making I wanted to be.” Strongly disagree 12 10.81 Somewhat disagree 16 14.41 Completely agree 26 23.42 Strongly agree 21 18.92 Completely disagree 12 10.81 Somewhat agree 20 18.02 “My doctor or midwife helped me understand all the information.” Somewhat agree 27 24.32 Completely agree 31 27.93 Strongly agree 32 28.83 Completely disagree 6 5.41 Strongly disagree 3 2.70 Somewhat disagree 8 7.21 “My doctor or midwife respected my choices.” Somewhat disagree 5 4.50 Strongly agree 37 33.33 Completely agree 36 32.43 Somewhat agree 22 19.82 Completely disagree 3 2.70 Strongly disagree 4 3.60 “My doctor or midwife told me that there are different options for my maternity care.” Somewhat disagree 13 11.71 Somewhat agree 32 28.83 Completely agree 23 20.72 Strongly agree 23 20.72 - - 182 Completely disagree 6 5.41 Strongly disagree 10 9.01 “I was given enough time to thoroughly consider the different care options.” Strongly disagree 9 8.11 Somewhat disagree 12 10.81 Strongly agree 25 22.52 Somewhat agree 30 27.03 Completely agree 26 23.42 Completely disagree 5 4.50 “My doctor or midwife explained the advantages/ disadvantages of the maternity care options.” Strongly disagree 8 7.21 Somewhat disagree 21 18.92 Completely agree 24 21.62 Somewhat agree 29 26.13 Strongly agree 19 17.12 Completely disagree 6 5.41 “I was able to choose what I considered to be the best care options.” Somewhat disagree 8 7.21 Somewhat agree 29 26.13 Completely agree 31 27.93 Strongly agree 31 27.93 Strongly disagree 4 3.60 Completely disagree 4 3.60 Note. Due to rounding errors, percentages may not equal 100%. - - 183 APPENDIX F Frequency Tables for Mother’s Autonomy in Decision-Making Index, by Item, Parts A, B and C Frequency Table for MORi – Part A, Responses by Item Variable n % “Overall while making decisions about my pregnancy or birth care…” “I felt comfortable asking questions.” Agree 33 29.73 Somewhat agree 12 10.81 Strongly agree 49 44.14 Somewhat disagree 6 5.41 Disagree 3 2.70 Strongly disagree 1 0.90 Somewhat Agree 1 0.90 “I felt comfortable declining care that was offered.” Somewhat agree 32 28.83 Somewhat disagree 17 15.32 Agree 24 21.62 Strongly agree 20 18.02 Disagree 7 6.31 Strongly disagree 4 3.60 Somewhat Agree 1 0.90 “I felt comfortable accepting the options for care that my doctor or midwife recommended.” Somewhat agree 19 17.12 Somewhat disagree 5 4.50 Agree 48 43.24 Strongly agree 31 27.93 Disagree 2 1.80 “I felt pushed into accepting the options my doctor or midwife recommended.” Somewhat disagree 22 19.82 Somewhat agree 18 16.22 Strongly agree 8 7.21 Disagree 32 28.83 - - 184 Strongly disagree 16 14.41 Agree 8 7.21 Somewhat Agree 1 0.90 “I chose the care options that I received.” Somewhat agree 24 21.62 Agree 41 36.94 Strongly agree 21 18.92 Disagree 3 2.70 Somewhat disagree 11 9.91 Strongly disagree 4 3.60 Somewhat Agree 1 0.90 “My personal preferences were respected.” Somewhat agree 17 15.32 Somewhat disagree 6 5.41 Agree 37 33.33 Strongly agree 36 32.43 Strongly disagree 6 5.41 Disagree 2 1.80 Somewhat Agree 1 0.90 “My cultural preferences were respected” Somewhat agree 11 9.91 Agree 45 40.54 Somewhat disagree 2 1.80 Strongly agree 42 37.84 Disagree 1 0.90 Strongly disagree 4 3.60 Note. Due to rounding errors, percentages may not equal 100%. - - 185 Frequency Table for MORi – Part B, Responses by Item Variable n % “During my pregnancy I felt that I was treated poorly by my doctor or midwife because of…” “My race, ethnicity, cultural background or language.” Disagree 10 9.01 Strongly disagree 84 75.67 Somewhat agree 4 3.60 Strongly agree 2 1.80 Somewhat disagree 5 4.50 “My sexual orientation and / or gender identity.” Disagree 11 9.91 Strongly disagree 90 81.08 Strongly agree 1 0.90 Somewhat disagree 2 1.80 “My type of health insurance or lack of insurance” Disagree 12 10.81 Strongly disagree 87 78.38 Strongly agree 1 0.90 Somewhat disagree 3 2.70 Agree 1 0.90 Somewhat agree 1 0.90 “A difference of opinion with my caregivers about the right care for myself or my baby” Somewhat agree 4 3.60 Somewhat disagree 9 8.11 Strongly agree 7 6.31 Strongly disagree 66 59.46 Disagree 14 12.61 Agree 4 3.60 - - 186 Frequency Table for MORi Questionnaire – Part C, Responses by Item Variable n % “During my pregnancy I held back from asking questions or discussing my concerns because…” “My doctor or midwife seemed rushed.” Somewhat disagree 14 12.61 Strongly agree 14 12.61 Disagree 26 23.42 Somewhat agree 23 20.72 Strongly disagree 20 18.02 Agree 8 7.21 “I wanted maternity care that differed from what my doctor or midwife recommended” Somewhat disagree 13 11.71 Strongly agree 7 6.31 Disagree 35 31.53 Strongly disagree 34 30.63 Somewhat agree 9 8.11 Agree 7 6.31 “I thought my doctor or midwife might think I was being difficult.” Somewhat agree 27 24.32 Agree 8 7.21 Strongly agree 6 5.41 Somewhat disagree 12 10.81 Strongly disagree 30 27.03 Disagree 22 19.82 - - 187 APPENDIX G Semi-Structured Interview Guide Research Protocol: Mistreatment in Childbirth: A mixed-methods approach to understanding the mental health sequelae of mistreatment in maternity care among a diverse cohort of birthing persons in New York City. Principal Researcher: Anika F. Alix, M.S. Doctoral Candidate in the Global Mental Health Lab at Teachers, College, Columbia University. Email: afa2129@tc.columbia.edu Phone #: 248-894-4241 INTRODUCTION WELCOME Thank you for agreeing to this interview. I appreciate your willingness to participate. INTRODUCTIONS • Interviewer • Interview Subject - name, age, date of childbirth, location of childbirth PURPOSE OF INTERVIEW The reason we are holding this interview is to learn more about your experience of giving birth, including the expectations you had before giving birth, how you felt your birth went, your perceptions of your treatment team, and the emotional and psychological impact you experienced following your birth. Specifically, we are interested in understanding how, if at all, the experiences of disrespect or mistreatment in labor may have shaped your birth experience. Your input, experience and opinions are integral to helping us understand how to help women feel prepared for childbirth, how to support their emotional well-being during and after birth, and to help us understand how to ensure birthing people experience healthy and satisfactory births. GROUND RULES 1. What we discuss in this interview will remain private. The meeting will be audio recorded because we want to capture everything you say but your name will not be attached to the interview or the transcript. If you are not comfortable being audio recorded, you do not have to complete this interview. 2. There are no right or wrong answers - we are just hoping to expand our understanding of your experience. 3. As a participant in this study, you have signed the consent form, but participation is voluntary and you are able to stop if at any time you feel uncomfortable and no longer wish to participate. If you have any questions about the study or this interview, please let us know. 4. We ask that you turn off any phones and pagers. If you cannot and if you must respond to a call, please let me know and we can pause. Finally, we do ask that you take this call in a private space. - - 188 QUESTIONS Engagement Questions 1. Today, I am going to begin by asking you a bit about yourself, then I will invite you to share your experience of giving birth, including childbirth preparation, experience of delivery, aftercare, and hear from you what factors you think impacted those experiences. As a reminder, you may end the interview at any time and you may skip any questions you do not wish to answer. Do you have any questions? 2. Why don’t we begin by asking a bit about your background (where from, age, job, date of recent childbirth, number of children). 3. Talk to me a bit about why you were interested in speaking with me more about your birth experience. 4. What do you hope to get out of this interview? 5. How did you learn about the study? Exploration Questions Domain A: Birth stories & narratives patient birth stories and narratives 1. Tell me about your most recent childbirth experience. 2. Described to me what happened in your labor. Domain B: Preparation, knowledge, and expectations before childbirth 1. Talk to me about how you felt leading up to your labor and delivery. 2. Some women take childbirth courses at the hospital or at local centers to prepare for birth, others speak to family members or other mothers. What was your experience of preparing for labor and delivery, if at all? 3. Describe for me what advice you would give pregnant people about giving birth in a New York City Hospital. 4. Before you gave birth, how did you imagine it? 5. Describe for me your postpartum experience. 1. Did you take any newborn care classes/postpartum education? 2. Did you plan to have any help in your home? Domain C: Factors which participants believe influenced the course, experience & outcomes of their birth 1. Talk to me about your birth outcomes i.e. (interventions you received in labor, your aftercare, and your delivery). 1. How do you feel about the outcomes of your birth? 2. You have indicated that you felt mistreated or disrespected in some way during your childbirth, describe to me what you think impacted this experience. List as many as you can think of. 3. Talk to me about the role you played in making decisions during your labor. 4. Describe for me what you would change about your birth experience. - - 189 Domain D: Perceptions of care and treatment 1. Tell me about the care you received from your treatment team (doctor, nurses, etc.) both during your labor and delivery, and after you gave birth, while in the hospital. 2. Tell me about your doctors and nurses; how was your relationship with them? 3. How were disagreements around your care or the baby’s care handled, if any? 4. Are there any moments in your medical treatment or healthcare that stand-out in your mind, that you would like to share? Domain E: Perceived emotional and psychological impacts of their birth experience 1. It is helpful for me to understand a bit about your mental and emotional health before you gave birth. Describe for me how you felt emotionally during your pregnancy and before your pregnancy. 2. Many women experience changes in how they feel, how they behave or how they function, after giving birth. Talk to me about any changes you have experienced, since giving birth. 1. Help me understand these changes from your perspective, what do you think caused them? 2. Have you sought any help to address these issues? With whom/where? 3. Some women feel that their birth experience impacts them emotionally, psychologically or even spiritually. Describe for me a time when you realized your birth had impacted you in some way. Exit Questions 1. We have discussed some difficult material today. I appreciate that you shared your story with me. I wonder, how are you feeling after this interview? 2. Is there anything that has not come up today that you would like to add or discuss? CLOSING Thank you so much for participating in this interview. We truly appreciate your time and the feedback that you gave. Your thoughts will be very helpful for future research and the future of women’s health right here in New York City. If you think of anything that you would like to add please feel free to email me and we can set up a time to speak. - - 190 APPENDIX H Final Codebook CNYC Study Research Protocol: Mistreatment in Childbirth: A mixed-methods approach to understanding the mental health sequelae of mistreatment in maternity care among a diverse cohort of birthing persons in New York City. Principal Researcher: Anika F. Alix, M.S. Doctoral Candidate in the Global Mental Health Lab at Teachers, College, Columbia University. Email: afa2129@tc.columbia.edu Phone #: 248-894-4241 Teachers College, Columbia University 525 West 120th Street New York NY 10027 © 9/15/2023 Anika F. Alix, M.S. All Rights Reserved - - 191 Final Codebook Unequal/Unfair Treatment: Care delivery, treatment from providers, experience of care as being unequal, different from others • Stigma and Discrimination: Based on Sociodemographic Characteristics o Discrimination based on ethnicity/race/religion o Discrimination based on age o Discrimination based on socioeconomic status o Discrimination based on insurance status o Discrimination Based on Medical Conditions/Health Status: (i.e. HIV status, obesity, Covid status, body shaming as a form of poor treatment, etc). • Special Treatment: Receiving special care, treatment that is better than what other patients received • Protective factors of unequal treatment: factors perceived by mother to protect her against mistreatment due to unequal/unfair treatment • Mental Effects of unequal treatment: Emotional, psychological, mental effects of unequal/unfair treatment Failure to Meet Professional Standards of Care • Lack of informed consent process • Physical Examinations and Procedures o Botch jobs: Procedures are poorly executed, wrongly executed, etc o Pain is poorly managed o Performance of unwanted operations, checks or procedures o Pressure: Provider is pressuring patient to undergo unwanted procedures or procedures they are not sure they want • Neglect and Abandonment: Neglect, abandonment, inattentive care, or long delays in care (long delays in nurses checking in, etc) • Variability In Care Delivery: Lack of consistency in care delivery within same facility or within the same treatment team (ie one nurse gives advil every 3 hours, another every 6 hours) • Unethical: Behavior that goes against normal ethical and moral obligations such as taking advantage of, being greedy Poor Rapport between Patient and Providers • Poor communication: Staff or care team communicating or interacting in a way that is unhelpful, poor, unprofessional, or even harmful, including poor information-sharing o Dismissal of patient’s concerns: Staff not taking concerns, requests seriously o Poor staff attitudes: Behavioral, negative tone, negative non-verbal communication of providers (including microaggressions, rolling eyes, etc.) • Disagreements or Dispute over care: Women's perceptions of disagreeing with providers in their care provision o Coercion: Providers intimidating them, saying something bad will happen, threatening, manipulating them in order to try to persuade birther/partner to do something they don't want to by - - 192 • Lack of Supportive Care: Treatment team does not create emotionally supportive environment, does not provide physical support o Birth companions compensating for lack of support from treatment team • Provider Power: Providers role as authority figures impacting relationship (ie “I don't think she liked to be questioned, and I don't think that she like people who had their own ideas of how they wanted to be”) “Best Practices” Standards of Care: Positive rapport experiences; what makes birth positive; what is expected • Communication: good communication of what is happening, using respectful language (verbal) • Attentive Care: Nurses, attendings, docs, etc. are attentive to needs and requests of birthing person; responsive • Procedures: Procedures given appropriately, without problems • Consent: Good experiences of being asked for permission to do a procedure Autonomy • Gender: Descriptions of control and power in labor in relation to gender (who had control, keeping control, giving away control, others taking control, women’s roles in birth/mothering). o Objectification of women: Women treated as “a means to an end,” women’s bodies being the focus instead of the woman herself (ie the body being more important than the person) o Women treated as passive participants during childbirth: Women’s opinions not being asked, doctors directing questions to partner instead of the woman, etc. • Denial of food, fluids, or mobility • Advocacy: Women or partners advocating for themselves during birth; efforts to have their voices heard • Resistance: Women challenging or questioning authority, decisions, processes in the treatment team/hospital setting o Consequences of challenging / questioning authority: Outcomes of resistance including poor treatment, loss of services, etc. • Sociodemographic: Race, identity, SES, health status as threatening, interacting with one’s independence in birth • Vulnerability: Descriptions of feeling or being vulnerable in birth Hospital or Health-System Conditions and Factors: Hospital-specific factors that define one’s birth experience, delivery of care or other, including constraints or hospital-specific variables • Constraints: Hospital factors that limit birth choices, experiences, types of care o Different training/education levels impacting care: Ex: teaching hospitals, care being different from a med student versus an attending o Provider changes: Ex: shift changes, provider on vacay o Physical condition of facilities (including lack of space) o Staffing shortages • Interventions: Interventions in birth given in hospital settings (i.e. pitocin drip, breaking water. etc). - - 193 • Efforts to cope: Efforts to control for, avoid or deal with hospital-related factors (i.e. waiting to go to hospital until very last minute, switching hospitals) • Financials: Choice of where to birth determined by financial factors o Insurance: determined by type of insurance • Birth location: Choice of where to birth (ie a different hospital, a birth center, at home, etc) impacted by poor hospital, care, treatment experiences in the past Knowledge & Preparation: Knowledge or preparation for the birth or postpartum in the form of childbirth education, facts, family information sharing, having a birth plan, advice-seeking • Helpful: Knowledge and prep helpful, effective in getting birth you wanted, staying safe • Lack of: A lack of knowledge, information, preparation for birth or postpartum experience perceived as impacting their experience • Life-saving: Sharing and/or receiving information perceived as dire, important to health of mothers or the self, potentially life-saving • Privilege/Access: Childbirth/postpartum knowledge or preparation as related to access, privilege, status (ie patient cannot access childbirth classes even if wants to due to cost, cannot access them due to insurance etc) • Lived experience: Past birth or labor experience (ie knowledge gained from actually having gone through it before) as impacting choices or experience in birth/postpartum • Self-Knowledge: Knowing yourself, knowing what you want, knowing what you are uncomfortable with (like intuition, knowing what you are comfortable with, listening to yourself) Understanding Mistreatment: Perceptions and experiences of poor birth experience and impacts of it • Make Meaning from Their Experience: Participant’s efforts to find meaning in their childbirth/postpartum experience through spirituality, resilience, advocacy, hindsight (i.e “It was hard but I learned how strong I was.”) or to make meaning out of their experience o Information Sharing: Motivation to share one’s own experience to help other birthing people, create external change in the world, in others, etc. • Make Sense: Participant’s efforts to make sense of what happened to them, through explanation, research, finding facts, asking other mothers (i.e. “I guess it happened that way because the hospital has to do that procedure,” or “I learned that other mothers also had that happen so it’s pretty normal actually.”) o Normalizing Poor Treatment: • Outcome focused: Narrative focused on or defined by the outcome rather than process of what happened (The Ends Justify the Means) (i.e. the mom’s health outcomes, baby’s health outcomes, how it “turned out” instead of what it was like going through it, etc). • Expectations v Reality: Expectations of one’s birth or postpartum as compared to what actually happened, or what ended up happening • Mental health: The relationship between emotional health and mistreatment (bidirectional) • Physical health: The relationship between mistreatment and physical health/medical health (including injury, morbidity, mortality) (bidirectional) - - 194 APPENDIX I Table 13 Moderation Analysis Table with MORI_Total Predicted by Race_Scale Moderated by Diabetes Predictor B SE β t p (Intercept) 67.72 1.22 0.00 55.36 < .001 Race_Scale 6.65 2.88 0.23 2.31 .023 Diabetes-yes -4.23 4.43 -0.09 -0.95 .342 Race_Scale:Diabetes-yes -7.98 10.25 -0.08 -0.78 .438 Table 14 Moderation Analysis Table with MORI_Total Predicted by Race_Scale Moderated by Hypertension Predictor B SE β t p (Intercept) 67.70 1.25 0.00 53.98 < .001 Race_Scale 7.23 2.99 0.25 2.42 .017 Hypertension-yes -3.06 3.60 -0.08 -0.85 .398 Race_Scale:Hypertension-yes -9.01 7.82 -0.12 -1.15 .252 Table 15 Moderation Analysis Table with MORI_Total Predicted by Race_Scale Moderated by Overweight Predictor B SE β t p (Intercept) 67.58 1.26 0.00 53.52 < .001 Race_Scale 5.32 3.01 0.19 1.77 .080 Overweight-yes -1.13 3.62 -0.03 -0.31 .756 Race_Scale:Overweight-yes 4.46 7.87 0.06 0.57 .573 - - 195 Table 16 Moderation Analysis Table with MORI_Total Predicted by Race_Scale Moderated by PR_Total Predictor B SE β t p (Intercept) 67.01 1.25 0.00 53.78 < .001 Race_Scale 6.66 2.96 0.23 2.25 .027 PR_Total -0.02 0.16 -0.01 -0.11 .916 Race_Scale:PR_Total 0.56 0.33 0.17 1.69 .094 Table 17 Moderation Analysis Table with MORI_Total Predicted by C_section_Emergency Moderated by Childbirth_Class_Lifetime Predictor B SE β t p (Intercept) 62.65 2.41 0.00 25.96 *< .001 C_section_Emergency -0.38 4.83 -0.01 -0.08 .937 Childbirth_Class_Lifetime “No” 12.34 4.59 0.42 2.69 *.011 C_section_Emergency:Childbirth_Class_ “No” -5.66 9.73 -0.10 -0.58 .565 Table 18 Moderation Analysis Table with MORI_Total Predicted by Unwanted_Interventions Moderated by Birth_Plan Predictor B SE β t p (Intercept) 66.66 1.22 0.00 54.65 *< .001 Unwanted_Interventions -12.46 2.64 -0.45 -4.73 *< .001 Birth_Plan “No” 5.11 2.94 0.17 1.74 .085 Unwanted_Interventions: Birth_Plan “No” 10.08 8.55 0.12 1.18 .241 - - 196 Table 19 Moderation Analysis Table with MORI_Total Predicted by Unwanted_Interventions Moderated by Caucasian Yes/No Predictor B SE β t p (Intercept) 67.29 1.04 0.00 64.55 *< .001 Unwanted_Interventions -13.00 2.39 -0.47 -5.43 *< .001 Caucasian Yes/No 7.32 2.46 0.26 2.98 *.004 Unwanted_Interventions:Caucasian Yes/No 7.88 5.96 0.11 1.32 .189 Table 20 Moderation Analysis Table with MORI_Total Predicted by Unwanted_Interventions Moderated by Childbirth_Class_Lifetime Predictor B SE β t p (Intercept) 66.59 1.21 0.00 55.18 *< .001 Unwanted_Interventions -12.11 2.67 -0.43 -4.54 *< .001 Childbirth_Class_Lifetime “No” 4.68 2.85 0.15 1.64 .104 Unwanted_Interventions:Childbirth_Class_ “No” 2.28 7.41 0.03 0.31 .758 Table 25 Moderation Analysis Table with MADM_Total Predicted by Unwanted_Interventions Moderated by Childbirth_Class_Lifetime Predictor B SE β t p (Intercept) 29.97 0.79 0.00 37.84 *< .001 Unwanted_Interventions -7.24 1.73 -0.40 -4.18 *< .001 Childbirth_Class_Lifetime “No” 2.52 1.86 0.13 1.36 .178 Unwanted_Interventions:Childbirth_Class_ “No” -2.03 4.86 -0.04 -0.42 .677 - - 197 Table 26 Moderation Analysis Table with MADM_Total Predicted by C_section_Emergency Moderated by Childbirth_Class_Lifetime Predictor B SE β t p (Intercept) 28.72 1.60 0.00 17.89 < .001 C_section_Emergency 2.90 3.21 0.17 0.90 .372 Childbirth_Class “No” 3.77 2.97 0.21 1.27 .213 C_section_Emergency:Childbirth_Class “No” -8.57 6.38 -0.26 -1.34 .188 Table 28 Moderation Analysis Table with PHQ-9 Predicted by Mental_Illness_Hx Moderated by MORI Total Predictor B SE β t p (Intercept) 4.69 0.36 0.00 12.91 < .001* Mental_Illness_Hx 0.50 0.73 0.06 0.69 .491 MORI_Total -0.12 0.03 -0.38 -4.09 < .001* Mental_Illness_Hx:MORI_Total 0.04 0.06 0.06 0.61 .545 Table 29 Moderation Analysis Table with PHQ-9 Predicted by Mental_Illness_Hx Moderated by MADM_Levels Predictor B SE β t p (Intercept) 3.53 1.54 0.00 2.29 .024* Mental_Illness_Hx 2.00 3.14 0.25 0.64 .525 MADM_Levels (Moderate Pt Autonomy) 0.86 1.65 0.11 0.52 .605 MADM_Levels (High Pt Autonomy) 1.23 1.67 0.15 0.74 .462 MADM_Levels (Very Low Pt Autonomy) 3.43 2.08 0.23 1.65 .103 Mental_Illness_Hx:MADM (Moderate) -0.83 3.35 -0.07 -0.25 .805 Mental_Illness_Hx:MADM (High) -1.18 3.38 -0.09 -0.35 .729 Mental_Illness_Hx:MADM (Very Low)) -4.50 4.21 -0.16 -1.07 .288 - - 198 Table 30 Moderation Analysis Table with PCL-5 Predicted by Mental_Illness_Hx Moderated by MADM_Levels Predictor B SE β t p (Intercept) 12.05 4.67 0.00 2.58 .011* Mental_Illness_Hx 2.00 9.62 0.08 0.21 .836 MADM_Levels (Moderate Pt Autonomy) -3.90 5.02 -0.15 -0.78 .439 MADM_Levels (High Pt Autonomy) -2.47 5.07 -0.10 -0.49 .627 MADM_Levels (Very Low Pt Autonomy) 12.54 6.36 0.27 1.97 .051 Mental_Illness_Hx:MADM (Moderate) 1.23 10.30 0.03 0.12 .905 Mental_Illness_Hx:MADM (High) 1.10 10.38 0.03 0.11 .916 Mental_Illness_Hx:MADM (Very Low) -3.25 12.91 -0.04 -0.25 .802 Table 31 Moderation Analysis Table with PCL5_Total Predicted by Mental_Illness_History Moderated by MORI Predictor B SE β t p (Intercept) 10.33 1.00 0.00 10.29 < .001* Mental_Illness_Hx 2.08 2.01 0.08 1.03 .303 MORI_Total -0.62 0.08 -0.61 -7.61 < .001* Mental_Illness_Hx:MORI_Total. -0.11 0.16 -0.05 -0.69 .493 Table 32 Moderation Analysis Table with Suicidal Sx Predicted by Mental_Illness_Hx Moderated by MORI_Total Predictor B SE β t p (Intercept) 0.06 0.02 0.00 2.46 *.016 Mental_Illness_Hx -0.009 0.05 -0.02 -0.18 .856 MORI_Total -0.003 0.002 -0.14 -1.42 .159 Mental_Illness_Hx:MORI_Total -0.003 0.004 -0.07 -0.73 .467 - - 199 Table 33 Moderation Analysis Table with PHQ-9 Predicted by Birth_Plan Moderated by MADM_Levels Predictor B SE β t p (Intercept) 3.81 1.37 0.00 2.79 *.006 Birth_Plan 2.33 4.22 0.25 0.55 .582 MADM (Moderate Pt Autonomy) 0.54 1.49 0.07 0.36 .716 MADM (High Pt Autonomy) 0.91 1.51 0.11 0.60 .550 MADM (Very Low Pt Autonomy) 3.41 2.01 0.23 1.70 .093 Birth_Plan:MADM (Moderate Pt Autonomy) -3.16 4.46 -0.22 -0.71 .480 Birth_Plan:MADM (High Pt Autonomy) -2.46 4.45 -0.18 -0.55 .582 Birth_Plan:MADM (Very Low Pt Autonomy) -4.62 6.01 -0.11 -0.77 .444 Table 34 Moderation Analysis Table with MORI_Total Predicted by Birth Plan Moderated by PHQ_Total Predictor B SE β t p (Intercept) 4.79 0.37 0.00 12.97 *<.001 Birth_Plan -1.53 0.93 -0.16 -1.64 0.10 MORI_Total -0.14 0.03 -0.43 -4.59 *<.001 Birth_Plan:MORI_Total 0.10 0.08 0.12 1.23 0.22 Table 36 Moderation Analysis Table with MORI_Total Predicted by C_section_Emergency Moderated by PHQ_Total Predictor B SE β t p (Intercept) 4.44 0.71 0.00 6.22 *<.001 C_section_Emergency -1.31 1.46 -0.14 -0.90 0.37 MORI_Total -0.12 0.05 -0.35 -2.20 *0.03 C_section_Emergency:MORI_Total -0.07 0.11 -0.09 -0.59 0.56 - - 200 Table 37 Moderation Analysis Table with PHQ_Total Predicted by C_section_Emergency Moderated by MADM_Levels Predictor B SE β t p (Intercept) 2.26 3.08 0.00 0.74 .468 C_section_Emergency 0.67 5.50 0.07 0.12 .904 MADM (Moderate Patient Autonomy) 2.40 3.33 0.26 0.72 .478 MADM (High Patient Autonomy) 1.98 3.32 0.22 0.60 .555 MADM (Very Low Patient Autonomy) 2.77 3.97 0.19 0.70 .491 C_section_Emergency:MADM (Moderate) -1.76 6.16 -0.11 -0.29 .777 C_section_Emergency:MADM (High) 0.58 6.02 0.04 0.10 .923 C_section_Emergency:MADM (Very Low) -7.33 7.77 -0.24 -0.94 .353 Table 38 Moderation Analysis Table with PHQ_Total Predicted by NICU Moderated by MADM_Total Predictor B SE β t p (Intercept) 4.59 0.39 0.00 11.71 *< .001 NICU -0.26 0.99 -0.03 -0.26 .794 MADM_Total -0.009 0.05 -0.02 -0.19 .853 NICU:MADM_Total -0.21 0.12 -0.18 -1.77 .080 Table 39 Moderation Analysis Table with MORI_Total Predicted by NICU Moderated by PHQ_Total Predictor B SE β t p (Intercept) 4.44 0.714 0.00 6.2 *<.001 C_section_Emergency_Dummy -1.31 1.463 -0.14 -0.89 0.37 MORI_Total -0.12 0.0537 -0.35 -2.20 *0.03 C_section_Emergency_Dummy:MORI_Total -0.07 0.111 -0.09 -0.59 0.57 - - 201 Table 40 Moderation Analysis Table with MORI_Total Predicted by NICU Moderated by PCL5_Total Predictor B SE β t p (Intercept) 10.31 1.01 0.00 10.16 *< .001 NICU -0.34 2.53 -0.01 -0.13 .893 MORI_Total -0.62 0.08 -0.61 -7.57 *< .001 NICU:MORI_Total -0.10 0.21 -0.04 -0.50 .622 Table 41 Moderation Analysis Table with PCL_5 Total Predicted by NICU Moderated by MADM_Total Predictor B SE β t p (Intercept) 10.08 1.24 0.00 8.15 *< .001 NICU 0.02 3.10 0.0008 0.01 .994 MADM_Total -0.31 0.16 -0.19 -1.96 .053 NICU:MADM_Total -0.64 0.36 -0.18 -1.77 .081 Table 42 Moderation Analysis Table with MORI_Total Predicted by C_section_Emergency Moderated by PCL5_Total Predictor B SE β t p (Intercept) 9.85 1.70 0.00 5.77 *<.001 C_section_Emergency 2.55 3.48 0.09 0.73 0.47 MORI_Total -0.72 0.13 -0.68 -5.61 *<.001 C_section_Emergency:MORI_Total -0.45 0.26 -0.21 -1.72 0.09 - - 202 Table 43 Moderation Analysis Table with PCL5_Total Predicted by C_section_Emergency_Scale Moderated by MADM_Levels Predictor B SE β t p (Intercept) 2.43 8.79 0.00 0.28 .784 C_section_Emergency 6.00 15.89 0.21 0.38 .708 MADM (Moderate Pt Autonomy) 9.93 9.55 0.35 1.04 .307 MADM (High Pt Autonomy) 4.56 9.55 0.16 0.48 .637 MADM (Very Low Pt Autonomy) 18.90 11.43 0.42 1.65 .109 C_section_Emergency:MADM (Moderate) 7.68 17.80 0.16 0.43 .669 C_section_Emergency:MADM (High) 2.43 17.51 0.05 0.14 .891 C_section_Emergency:MADM (Very Low) -18.33 22.47 -0.20 -0.82 .421 - 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