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Transgender patients’ perceptions of healthcare: A study of gender minority stress and resilience factors in predicting healthcare behavioral intentions

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Transgender patients’ perceptions of healthcare: A study of gender minority stress and resilience factors in predicting healthcare behavioral intentions

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Content TRANSGENDER PERCEPTIONS OF HEALTHCARE 1

Transgender Patients’ Perceptions of Healthcare:
A Study of Gender Minority Stress and Resilience Factors in Predicting Healthcare Behavioral
Intentions

by

Sebastian N. Smoak

A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION

May 2023

Copyright 2023 Sebastian Nicholas Smoak
TRANSGENDER PERCEPTIONS OF HEALTHCARE 2

Acknowledgements
I want to take a moment to thank the incredible support structure that has allowed me to
achieve a lifelong goal. To my dissertation chair, Dr. Nicole MacCalla, I am incredibly thankful
to have had your mentorship and support throughout the dissertation process. Your patience,
kindness, and willingness to support my academic goals is a testament to educators. To my
dissertation committee, Dr. Hirabayashi and Dr. Kho, thank you so much for pushing me to
make this the best possible product it can be and for your guidance and coaching.
To the brave and caring individuals who participated in this research, I cannot express my
gratitude for your vulnerability, willingness to share your experiences and feedback, and desire
to address healthcare disparities for the LGBTQIA+ community. The overwhelming support for
this study as demonstrated by the number of respondents was wonderfully surprising and
incredibly inspiring. The thoughtfulness of responses was eye-opening and directly shaped the
recommendations within this study. Thank you.
To my family back home in Texas, I know that the long hours and endless efforts
required of this program were made easier by the penchant for hard work that was instilled in me
at a young age. To my mother, I know that my ability to listen and show compassion to those
around me was a direct reflection of learning from your example of unconditional love. To my
siblings, I genuinely appreciate being able to lean on you for support and friendship during the
last several years. To the Reinhart family, your ongoing support and cheerleading since the very
beginning of the program has been integral my keeping on track and continuing to accomplish a
seemingly herculean feat. Most importantly, I have to acknowledge the substantial contribution
of my significant other, Jacob. Our common goal to build a life together has driven me more than
I thought possible. I am so very grateful for your patience and support throughout this process.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 3

Table of Contents
CHAPTER ONE: INTRODUCTION TO THE STUDY 10
Context and Background of the Problem 10
Purpose of the Project and Research Questions 12
Importance of the Study 13
Overview of Theoretical Framework and Methodology 14
Definitions 15
Organization of Dissertation 18
CHAPTER TWO: LITERATURE REVIEW 19
Historical Context of Transgender Rights and Healthcare 19
History of Transgender Legislation, Policy, and Rights 19
History of Transgender Medicine 22
Underlying Factors of Transgender Healthcare Engagement 24
Patient Sentiment and Medical Mistrust 24
Provider Behaviors and Impact of Educational Experiences 26
Existing Strategies to Improve Transgender Healthcare Engagement 27
National Transgender Medical Infrastructure 28
Standards of Care and Access to Care 28
Treatment Options 30
Conceptual Framework 31
Past Research Leveraging Theoretical Frameworks 32
Minority Stress Theory 32
Summary 34
CHAPTER THREE: METHODOLOGY 35
Research Questions 35
Overview of Design 36
Research Setting 37
The Researcher 38
Data Sources 39
Survey 39
Validity and Reliability 48
Ethics 48
CHAPTER FOUR: FINDINGS 50
Research Question 1 Findings 50
TRANSGENDER PERCEPTIONS OF HEALTHCARE 4

Finding 1: Respondents seek in-person care more than virtual care, but heavily depend on
virtual care options 51
Finding 2: Health-Seeking Behaviors vary across Race, Gender, Income, and Insurance
Coverage 52
Finding 3: Health Seeking Behaviors are influenced by the existence or lack of physician
support 66
Research Question 2 Findings 68
Theme 4: Concealment as a solution to receiving primary care 69
Theme 5: Financial insecurity and employment insecurity are barriers to receiving primary
care 70
Theme 6: Conflation of gender identity and mental health illnesses by primary care
providers act as barriers to receiving primary care 71
Theme 7: Community Connectedness as a facilitator to seeking primary healthcare 72
Research Question 3 Findings 74
Research Question 4 Findings 75
Finding 8: Transgender healthcare seeking behaviors are moderately predicted by a gender
minority stress and resilience explanatory model 75
Finding 9: Transgender healthcare receiving behaviors are minimally predicted by a gender
minority stress and resilience explanatory model 77
Finding 10: Transgender perceived physical health is minimally predicted by a gender
minority stress and resilience explanatory model 78
Finding 11: Transgender perceived mental health is moderately predicted by a gender
minority stress and resilience explanatory model 80
Summary 82
CHAPTER FIVE: RECOMMENDATIONS 83
Discussion of Findings 83
Recommendations for Practice 84
Recommendation 1: Establish a Use-Case for Low-Cost, Accessible, Multi-Modal
Healthcare 85
Recommendation 2: Establish a Coalition of Non-Profits and Government Agencies to
Educate Gender and Sexual Minorities on Navigating Local Healthcare Systems and
Defending Employment Rights 87
Recommendation 3: Incentivizing Human Rights Campaign Approach to Assessing and
Rewarding Gender-Affirming Communities 88
Recommendation 4: Petitioning the ACGME to Incorporate Physician Training and Pledges
into Continuing Medical Education Requirements 90
Recommendation 5: Address Pervasive Gender Concealment Behaviors within the
Transgender Community by Promoting Community Connectedness 91
TRANSGENDER PERCEPTIONS OF HEALTHCARE 5

Limitations and Delimitations 93
Recommendations for Future Research 94
Conclusion 95
References 97
Appendix A: Social Media Recruitment Plan and Fliers 111
Appendix B: Survey 113
Appendix C: Construct Map 120
Appendix D: Code Book for Open-Ended Survey Items 121
Appendix E: Pearson-Product Moment Q-Q Plots Support RQ#3 122

TRANSGENDER PERCEPTIONS OF HEALTHCARE 6

List of Tables
Table 1. Study Data Sources...................................................................................................... 37
Table 2. Survey Sample Characteristics ..................................................................................... 40
Table 3. Internal Consistency Measures for Gender Minority Stress and Resilience Scales ........ 43
Table 4. Construct Definitions ................................................................................................... 44
Table 5. Mean Scores for Health Seeking Behaviors by Race .................................................... 52
Table 6. Mean Scores for Health Perceptions by Race ............................................................... 52
Table 7. Mean Scores for Health Seeking Modalities by Race ................................................... 55
Table 8. Mean Scores for Health Seeking Behaviors by Insurance, Income, and Gender ........... 57
Table 9. Mean Scores for Health Perceptions by Insurance, Income, and Gender ...................... 60
Table 10. Mean Scores for Health Seeking Modalities by Insurance, Income, and Gender ....... . 60
Table 11. Open-Ended Survey Responses Related to Physician Support .................................... 67
Table 12. Sentiment Analysis Overview of Open-Ended Survey Responses by Parent Codes ... .68
Table 13. Open-Ended Survey Responses Related to Concealment ............................................ 70
Table 14. Open-Ended Responses Related to Financial Insecurity ............................................. 71
Table 15. Open-Ended Responses Related to Gender Identity and Mental Illness ...................... 72
Table 16. Open-Ended Survey Responses Related to Community Connectedness ..................... 73
Table 17. Correlation Matrix for Gender Minority Stress and Resilience Variables ................... 74
Table 18. Multivariate Stepwise Regression for DV Healthcare Sought ..................................... 77
Table 19. Multivariate Stepwise Regression for DV Healthcare Received ................................. 78
Table 20. Multivariate Stepwise Regression for DV Perceived Phsyical Health ......................... 80
Table 21. Multivariate Stepwise Regression for DV Perceived Mental Health ........................... 81
Table 22. Alignment of Gender Minority Drivers to Research Questions to Recommendations . 84
TRANSGENDER PERCEPTIONS OF HEALTHCARE 7

List of Figures
Figure 1. Sexual and Gender Minority Primary Care Utilization Framework ............................. 32
Figure 2. Mutiple Regression Model for Data Analysis ............................................................. 48
Figure 3. Breakdown of Healthcare Seeking Modalities ............................................................ 51
Figure 4. Correlation of Discrimination and Gender Rejection ................................................ 122
Figure 5. Correlation of Discrimination and Gender Victimization .......................................... 122
Figure 6. Correlation of Discrimination and Gender Non-Affirmation ..................................... 123
Figure 7. Correlation of Discrimination and Internalized Transphobia ..................................... 124
Figure 8. Correlation of Discrimination and Negative Expectations ......................................... 125
Figure 9. Correlation of Discrimination and Concealment ....................................................... 126
Figure 10. Correlation of Gender Rejection and Gender Victimization .................................... 127
Figure 11. Correlation of Gender Rejection and Gender Non-Affirmation ............................... 128
Figure 12. Correlation of Gender Rejection and Internalized Transphobia ............................... 129
Figure 13. Correlation of Gender Rejection and Negative Expectations ................................... 130
Figure 14. Correlation of Gender Rejection and Concealment ................................................. 131
Figure 15. Correlation of Gender Victimization and Internalized Transphobia......................... 132
Figure 16.Correlation of Gender Victimization and Negative Expectation ............................... 133
Figure 17. Correlation of Gender Victimization and Concealment ........................................... 134
Figure 18. Correlation of Gender Non-Affirmation and Internalized Transphobia.................... 135
Figure 19. Correlation of Gender Non-Affirmation and Negative Exepctations ....................... 136
Figure 20. Correlation of Gender Non-Affirmation and Concealment ...................................... 137
Figure 21. Correlation of Gender Non-Affirmation and Pride .................................................. 138
Figure 22. Correlation of Gender Non-Affirmation and Community Connectedness ................ 139
TRANSGENDER PERCEPTIONS OF HEALTHCARE 8

Figure 23. Correlation of Internalized Transphobia and Negative Expectations ....................... 140
Figure 24. Correlation of Internalized Transphobia and Concealment ...................................... 141
Figure 25. Correlation of Negative Expectations and Concealment .......................................... 142
Figure 26. Correlation of Negative Expectations and Pride ...................................................... 143
Figure 27. Correlation of Negative Expectations and Community Connectedness ................... 144
Figure 28. Correlation of Concealment and Pride .................................................................... 145
Figure 29. Correlation of Concealment and Community Connectedness .................................. 146
Figure 30. Correlation of Community Connectedness and Pride .............................................. 147

TRANSGENDER PERCEPTIONS OF HEALTHCARE 9

Abstract
This study applies the Gender Minority Stress and Resilience (GMSR) from academic
literature to understand primary healthcare behaviors and perceptions within the transgender
community. The purpose of the study is to examine how GMSR explanatory variables shape
transgender patient behaviors and perceptions related to primary healthcare utilization.
Specifically, this study aims to examine what health seeking behaviors look like for transgender
patients, how transgender patients describe their health seeking experiences, what the
relationships between GMSR variables are, and which GMSR factors, if any, predict transgender
patient attitudes and intentions toward primary care utilization. Using data collected from a
University of Southern California Institutional Review Board (IRB) approved survey of 213
transgender and gender non-conforming participants, the GMSR model was tested to examine
predictive power for health-seeking, health-receiving, perceived physical health, and perceived
mental health. Additionally, 42 open-ended survey responses were assessed for thematic
analysis. Findings from this study indicate that income, age, insurance coverage, and some
GMSR variables are able statistically significant in predicting healthcare behaviors and
perceptions. Notably, internalized transphobia, age, and income moderately predict a transgender
individuals perceived mental health. Open ended survey responses highlighted gender identity
concealment, financial insecurity, and conflation of mental illness and gender dysphoria as
inhibitors to care but identified community connectedness as a potential healthcare support. The
implications of this study highlight that gender minority stress, income, and insurance coverage
for transgender people drive healthcare disparities in the LGBTQIA+ community. This study
seeks to make public policy-based recommendations that would bridge the healthcare gap for
transgender patients seeking primary healthcare.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 10

CHAPTER ONE: INTRODUCTION TO THE STUDY
This paper addresses the challenge of promoting transgender patient utilization of
primary care healthcare services and treatment. This challenge is significant because, despite
increased risks for mental health issues and cancer, Lesbian, Gay, Bisexual, Transgender, Queer,
Intersex, and Asexual (LGBTQIA+) patients often opt out of seeking healthcare for fear of
discrimination, bias, or other negative healthcare experiences (Schuller & Crawford, 2020;
Haviland et al., 2020). A 2015 study of 452 transgender respondents living in Massachusetts
indicated 24% had experienced discrimination in health care settings in the last twelve months,
which resulted in a three-fold increased risk of postponement of care (Reisner et al., 2015).
While gender-affirming healthcare practices are slowly becoming more common to reduce SGM
healthcare disparities, a nationwide survey of 5,831 transgender individuals indicated that even
though 81% of respondents were insured through public or private means, 30.3% reported
avoiding emergency or preventative care due to prior mistreatments in their healthcare
experiences (Matouk & Wald, 2021;White Hughto et al., 2016). Despite attempts to modernize
and provide gender-affirming standards of care, additional research must be completed (Matouk
& Wald, 2021). Before substantive recommendations for fostering SGM healthcare engagement
can be developed, motivational factors and trends within the SGM community must be
researched and critically examined.
Context and Background of the Problem
Over the past several decades, awareness and support for the LGBTQIA+ community
have been slowly growing. For example, support for same-sex marriage was up to 61% in 2019,
as opposed to just 37% in 2009 (Pew Research, 2019). Moreover, a comprehensive, state-to-state
TRANSGENDER PERCEPTIONS OF HEALTHCARE 11

assessment of public policy equality for the LGBTQIA+ community shows some improvements
in sexual and gender minority (SGM) protections, but also noted that 45% of all SGMs in the US
live in states where legislation provides minimal protection for this community (LGBTQ
Map.org, 2022).
While awareness of the SGM community is rising in the United States, healthcare
disparities persist due to several factors such as fear of discrimination, perceived bias by
healthcare professionals, a lack of provider training, interpersonal fears of rejection, and lack of
awareness of how to acquire care (Puckett et al., 2018). In a study examining barriers to gender-
affirming healthcare, transgender respondents indicated financial resource limitations,
availability of care, and open bias/discrimination as the top barriers to gender-affirming care
(Puckett et al., 2018). A national transgender study in 2015 with over 27,000 participants
identified that 23% of respondents avoid seeking care due to fear of provider mistreatment or
harassment (Schuller & Crawford, 2020). In a study of 61 transgender people and 87 cisgender
women, transgender participants had significantly lower health knowledge, reported higher
discomfort interacting with healthcare providers, and lower likelihood of receiving genital exams
(pelvic exams, Pap smear, etc.) (Rahman et al., 2017). A study of 433 trans people in Canada
found that despite 83.1% of the sample having a physician, nearly half of respondents reported
discomfort with discussing trans health with their provider and nearly 38% of respondents
reported a past, negative trans-specific event (Bauer et al., 2015). Another literature review
examining barriers to cancer screening in the LGBTQIA+ community noted that alcohol and
tobacco use was higher among SGMs, which is a contributing factor to elevated cancer risk and
disproportionate cancer rates in LGBTQIA+ individuals (Haviland et al., 2020). Despite these
disproportionate cancer rates, the review found that provider/patient knowledge gaps, poor
TRANSGENDER PERCEPTIONS OF HEALTHCARE 12

patient psychosocial reactions, and educational attainment were all barriers to opting into cancer
screening (Haviland et al., 2020).While the study documented factors that contributed to
discomfort in patient-physician interactions, it did not gather which factors are associated with a
patient’s likelihood to return for follow-up treatment.
Despite documentation of healthcare disparities within the transgender population
specifically, studies have yet to focus on which factors most significantly contribute to outcomes
such as reported discomfort in interacting with healthcare providers. Despite growing literature
documenting transgender health disparities and lack of transgender healthcare utilization, studies
have yet to fully examine the impact of gender and minority stress and resilience factors and how
they shape transgender patient behaviors and attitudes.
Purpose of the Project and Research Questions
The purpose of the study is to examine how GMSR explanatory variables shape
transgender patient behaviors and perceptions related to primary healthcare utilization.
Specifically, this study aims to examine what health seeking behaviors look like for transgender
patients, how transgender patients describe their health seeking experiences, what the
relationships between GMSR variables are, and which GMSR factors, if any, predict transgender
patient attitudes and intentions toward primary care utilization. Recommendations for policy and
practice are generated to address the challenge of fostering transgender healthcare utilization and
reducing healthcare avoidance due to negative healthcare experiences. The research questions to
be addressed within this study are:
1) What does health-seeking behavior look like for transgender patients seeking primary
health care services?
TRANSGENDER PERCEPTIONS OF HEALTHCARE 13

2) How do transgender patients seeking primary health care services describe their
experience in relation to distal stressors, proximal stressors, and resilience factors?
3) What is the relationship between distal stress factors (gender-related discrimination,
gender-related rejection, gender-related victimization, and non-affirmation of gender
identity), proximal stress factors (internalized transphobia, negative expectations, and
concealment), and resilience factors (community connectedness and pride)?
4) To what extent do distal stress factors (gender-related discrimination, gender-related
rejection, gender-related victimization, and non-affirmation of gender identity),
proximal stress factors (internalized transphobia, negative expectations, and
concealment), and resilience factors (community connectedness and pride) predict
healthcare behaviors and perceptions?
Importance of the Study
This study is important because it increases our knowledge and understanding of factors
influencing transgender primary care utilization. By gaining an understanding of transgender
healthcare utilization factors, insights and recommendations for fostering transgender patient
primary care utilization can be developed. By reducing SGM healthcare avoidance, healthcare
systems can be empowered to address existing healthcare disparities, improve health knowledge,
and deconstruct historical healthcare barriers for LGBTQIA+ patients. As the field of gender-
affirming care is further developed to promote inclusivity and physician support for gender-
diverse populations, it is imperative that physicians and healthcare organizations understand
which factors most significantly impact a transgender patient’s likelihood to engage in primary
care services. By developing a thorough understanding of these predictive factors, healthcare
systems can generate policies that address the most urgent factors that may dissuade transgender
TRANSGENDER PERCEPTIONS OF HEALTHCARE 14

patients from seeking future care. In addressing factors that prevent transgender patient primary
care utilization, healthcare systems can begin to address the numerous health disparities plaguing
the transgender community.
Overview of Theoretical Framework and Methodology
The theoretical framework chosen for this study is Testa et al.’s (2015) Gender Minority
Stress and Resilience Model, which identifies distal and proximal stressors for gender and sexual
minorities and accounts for community connectedness and pride as protective factors that
mitigate these stressors. Distal stress factors outlined by Hendricks and Testa (2012) can be
thought of as the external, environmental actions and events that impact transgender and gender-
nonconforming people. In the case of transgender primary care, these factors may be the
improper use of pronouns by a physician or harassment by other patients in a waiting room.
Proximal stress factors are internalized factors such as internal transphobia, negative
expectations, and gender identity concealment that impact transgender and gender
nonconforming people’s health outcomes (Hendricks & Testa, 2012). In the case of transgender
primary care, these factors may be a lack of truthfulness in responses to the primary care
physician or nondisclosure of hormone treatment due to internalized transphobia. By considering
the impact of distal and proximal minority stressors on key variables of the expectancy value
theory model, a conceptual framework to critically examine how gender minority stress impacts
primary care utilization can be developed. Employing Gender and Minority Stress and Resilience
theory to address the challenge of improving transgender healthcare utilization is useful because
it leverages a framework that is common within GSM research and comprehensively accounts
for factors within the transgender patient setting that may influence healthcare behavioral
intentions. To make concrete recommendations for improving transgender patient healthcare
TRANSGENDER PERCEPTIONS OF HEALTHCARE 15

engagement, the predictive nature of each proposed independent variable and its relative level of
impact on the dependent variable (patient behavior) must be determined.
Creswell & Creswell (2018) outline that survey design is a powerful tool for testing
associations and relationships among variables of a given population by providing quantitative
data that can be statistically analyzed. As such, this dissertation adopts a quantitative research
methodology, employing a cross-sectional survey instrument for data gathering and a stepwise
regression model to analyze transgender patient healthcare utilization data. The survey will be
segmented to capture data on all seven of the proximal and distal stressors and both of the two
resilience factors of the Gender Minority Stress and Resilience model (Testa et al., 2015). The
survey recruited 215 participants, which will allowed for the determination of large and medium-
sized effects (Roper, 2022; Testa et al., 2015).
Definitions
Gender Affirmation
Gender affirmation can be defined as an interactive process between gender minorities
and those around them, as a person receives social recognition and support for their gender
identity and expression (Sevelius, 2012). Gender affirmation is well researched within the gender
minority population and has been shown to play a prominent role in confirming a sense of self
(Sevelius, 2012).
Gender-Affirming Healthcare
The gender-affirming model of care centers on assisting individuals in exploring and
actualizing their gender identity and focuses on the individual through patient education,
building patient support structures, social interventions, and medical interventions (Matouk &
Wald, 2021). Gender affirming care can range from hormone therapy to surgical procedures to
gender-affirming infrastructure such as medical intake software that accounts for personal
TRANSGENDER PERCEPTIONS OF HEALTHCARE 16

pronouns and ensures provider education on gender identity issues and treatments (Puckett et al.,
2018).
Gender Minority
The Centers for Disease Control and Prevention define a gender minority as someone
whose gender expression or identity differs from the sex initially assigned to that individual at
birth (CDC, 2019). Gender minorities are members of the broader SGM population.
Healthcare Avoidance
SGM healthcare avoidance is a concept derived from minority stress theory wherein
prolonged stigmatization results in psychological and physical health consequences, which in
turn deplete affective and cognitive reserves that can lead to LGBTQIA+ patients avoiding
seeking healthcare for fear of additional negative experiences (Hampton & Pachankis, 2018).
Health Disparity
The Center for Disease Control and Prevention define health disparities as inequitable
health risks and outcomes related to past and present unequal distribution of political, economic,
environmental, or social resources (CDC, 2020). Meade et al. (2014) identify that health
disparities are the result of factors or conditions rooted in lifestyle, culture, socioeconomic status,
and accessibility of resources.
Healthcare Disparity
A subset of Health Disparities, Healthcare Disparities are defined as the differences in
access to healthcare or the quality of healthcare provided (Meade et al., 2014). Healthcare
disparity is exemplified by the inability of the healthcare system to address the needs of specific
groups or populations (Meade et al., 2014).
Primary Care
The American Academy of Family Physicians (2019) defines Primary Care as the
provision of accessible and integrated health care services by health care teams. These health
care teams take a person-centered, team-based approach to addressing a large majority of
TRANSGENDER PERCEPTIONS OF HEALTHCARE 17

personal health needs such as health promotion, disease prevention, counseling and patient
education, health maintenance, and treatment of a variety of illnesses in collaboration with other
health professions and specialties (AAFP, 2019).
Sexual Minority
The Centers for Disease Control and Prevention define a sexual minority as someone
who has sexual contact with or is attracted to people of the same gender or someone who
identifies as a member of the lesbian, gay, or bisexual community (CDC, 2019). Sexual
minorities are members of the broader SGM population.
Sexual and Gender Minority (SGM)
The National Institutes of Health has adopted the term Sexual and Gender Minority
(SGM) to be inclusive of diverse populations of people whose gender identity, sexual
orientation, or reproductive development differs from physiological or cultural/societal norms
(SGMRO, 2017). While transgender individuals are considered members of the SGM
community, it is important to note that there are numerous sub-communities within the SGM
population.
Transgender
The Centers for Disease Control and Prevention define transgender people as individuals
who have a differing gender identity from their sex assigned at birth (CDC, 2019). Transgender
individuals are members of the broader gender minority and SGM populations. According to the
UCLA School of Law Williams Institute, .5%, or approximately 1.3 million people, of the U.S.
adult (18+) population identifies as transgender (Herman et al., 2022). Herman et al. (2022) note
that data from the Behavioral Risk Factor Survey indicate that of the 1.3 million adults
identifying as transgender further identify as transgender women (38.5%), transgender men
(35.9%), and gender nonconforming (25.6%).
TRANSGENDER PERCEPTIONS OF HEALTHCARE 18

Organization of Dissertation
In addition to the introduction and background information provided in this first chapter,
this dissertation is organized into four additional chapters. The second chapter consists of a
literature review of substantive studies and academic works in the field of transgender
healthcare. The third and fourth chapters address the study methodology and results,
respectively. Finally, the fifth chapter captures recommendations for fostering transgender
patient engagement with healthcare systems.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 19

CHAPTER TWO: LITERATURE REVIEW
To examine the challenge of fostering transgender healthcare utilization, an
understanding of the history of the transgender community, transgender rights, and the evolution
of transgender healthcare is critical. The history of transgender people informs research by
highlighting the challenges, social persecution, and lack of access to care that have characterized
the transgender experience. Beyond understanding the history of transgender rights and
medicine, a current state assessment of the transgender and healthcare relationship must be
attained. Patient medical mistrust factors perceived problematic provider behaviors, and an
incomplete national transgender healthcare infrastructure inform the problem space of
transgender patients receiving and utilizing healthcare services. Finally, the historical context of
the transgender experience and the underlying factors challenging transgender healthcare
engagement must be concisely modeled using relevant theoretical frameworks that have been
proven in the field of LGBTQIA+ public health research.
Historical Context of Transgender Rights and Healthcare
History of Transgender Legislation, Policy, and Rights
Dating back to the Civil Rights Act of 1964, the United States began to see the
introduction of new legislation to protect marginalized communities. These protections ranged
from religion, race, color, national origin, and sex but did not include gender protections for the
LGBTQIA+ community (Wahlert & Gill, 2017). In 1965, Title VII of the Civil Rights Act
established the Equal Employment Opportunity Commission (EEOC) which consisted of
presidential appointees who were specifically tasked with investigating acts of discrimination
(Wahlert & Gill, 2017). Since its inception in 1965, the EEOC has seen its authorities and
TRANSGENDER PERCEPTIONS OF HEALTHCARE 20

responsibilities expand and contract with each passing of legislation (Wahlert & Gill, 2017).
While the EEOC began to address discrimination from a federal perspective, the 1960s and
1970s were characterized by social stigma for the LGBTQIA+ community (Michelson &
Harison, 2020). An hour-long CBS newscast in 1967 characterized homosexuals as promiscuous,
mentally ill predators who represented a threat to children within their communities (Michelson
& Harison, 2020). In 1977, a Gallup poll found that only 43% of Americans felt that gay or
lesbian relations among consenting adults should be legal (The Gallup Organization, 2021).
While that figure has astoundingly increased to a total of 79% of Americans supporting gay and
lesbian relations in 2021, this societal support has been largely relegated to gay and lesbian
people not transgender people (The Gallup Organization, 2021).
In 2010, the passage of President Obama’s healthcare reform law, known as the
Affordable Care Act, provided healthcare insurance for many LGBTQIA+ people, extended
protections for sexual and gender minorities, and prevented insurers from denying gender
transition-related healthcare services (Gonzales & McKay, 2017). In addition to expanding
LGBTQIA+ healthcare protections under the ACA, President Obama repealed the Don’t Ask,
Don’t Tell policy in 2011 allowing gay and lesbian people to openly serve although these
protections did not extend to transgender service members (Dietert & Dentice, 2022). Despite
ongoing social controversy regarding transgender rights, in 2012 the EEOC, in keeping with
current case law, began prohibiting discrimination based on gender identity (Wahlert & Gill,
2017). In 2015, President Obama was the first elected executive to use the word “transgender” in
his State of the Union speech, representing a significant shift in the national dialogue for
transgender people (Mezey, 2020). In keeping with President Obama’s State of the Union speech
and stated commitment to expanding transgender protections, Defense Secretary Ash Carter
TRANSGENDER PERCEPTIONS OF HEALTHCARE 21

officially rescinded the ban on transgender military service in June 2016 (Dietert & Dentice,
2022). Following the state of the union speech, the Obama administration notably added
questions about sexual orientation and gender identity to national government surveys to address
LGBTQIA+ needs (Mezey, 2020). Finally, just two days before the administration ended,
President Obama’s cabinet issued guidance via the Prison Rape Elimination Act that prison
officials should determine housing decisions for transgender inmates based on gender identity if
possible (Mezey, 2020).
Beginning in 2017, the Trump presidency oversaw rollbacks in transgender protections
such as the Departments of Justice and Education withdrawing Obama-era Title IX guidance to
ensure transgender students were not discriminated against (Hughto et al., 2021). Later that year,
President Trump issued a memo to the Secretaries of Defense and Homeland Security banning
transgender individuals from joining the military which kicked off a wave of transgender
protection rollbacks at the state level ranging from public accommodations, housing, and
education protections (Hughto et al., 2021). A 2018 qualitative study of 969 gender non-
conforming people and sexual minority women indicated that nearly 90% of respondents felt
higher levels of concern after the election of President Trump and the shift in national discourse
(Drabble et al., 2019). Another study found that high levels of discrimination fear amongst
GSMs have negative implications for feelings of safety, which likely activates minority stress
when these fears are coupled with the stigmatizing rhetoric characteristic of President Trump’s
campaign and public discourse (Veldhuis et al., 2018). Heightened fears and concerns of the
GSM community coupled with President Trump’s ongoing rhetoric and opposition to
transgender military service marked the end of an era for transgender protections. President
Trump appointed several key cabinet officials, such as the Department of Education, Department
TRANSGENDER PERCEPTIONS OF HEALTHCARE 22

of Justice, Health and Human Services, and more, who had reputations opposing GSM rights
(Mezey, 2020). Under President Trump's appointees, government surveys from Housing and
Urban Development and HHS attempted, and in some cases were successful, in removing sexual
orientation and gender identity questions, thus concealing the LGBTQIA+ population from
survey findings (Mezey, 2020).
After a single term in office, the Trump administration transitioned to the Biden
administration which, while still early on, has sought to restore many of the Obama-era
protections for transgender people. In January 2021, immediately upon assuming office,
President Biden issued an executive order to prevent sexual orientation and gender identity
(SOGI) discrimination (Witt & Medina-Martinez, 2022). Two months later, Biden issued two
additional executive orders (insert order #s): one allowing transgender military service to include
coverage of medically necessary transition-related healthcare expenses and another preventing
SOGI discrimination in education settings thus emplacing federal protections for LGBTQIA+
students (Witt & Medina-Martinez, 2022).
History of Transgender Medicine
As early as the 1920s, Magnus Hirschfeld’s Institute fur Sexualwissenschaft offered
gender transition services for transgender patients under a medical paradigm that differentiated
between intersexuality and what was then called “transvestism,” or the desire to live as a gender
other than the one assigned at birth (Gill-Peterson, 2018). Hirschfield, a sexual reformist who
argued that sex and sexuality reside on a spectrum rather than a binary, championed a new way
of thinking about sex and gender in Germany (Slagstad, 2021). Hirschfield’s counterparts in the
United States lacked a similar medical understanding and relegated trans people as intersex
homosexuals, therefore denying them medical treatment and minimizing the emergence of trans
TRANSGENDER PERCEPTIONS OF HEALTHCARE 23

identities in medical research for the first half of the 20
th
century (Slagstad, 2021). In the late
1940s, John Money, a doctoral psychology student at Harvard University, visited a local care
center where he met a young patient who was assigned male at birth and raised as such, but due
to hormone resistance increasingly presented as female (Gill-Peterson, 2018). This led Money to
begin a body of work that promoted the clinical medicalization of intersex children, which in
turn laid the foundational concepts of gender and sex assignment as a medical procedure (Gill-
Peterson, 2018).
In 1952, Christine Jorgensen made headlines upon her return to the United States from
Copenhagen, Denmark where she received a series of operations and hormone treatment (Docter,
2008). Jorgensen, previously a male assigned at birth who grew up to serve in the military, was
not the first American to receive gender assignment medical care but is considered one of the
first people to bring the transgender agenda to the national stage (Docter, 2008). As concepts of
gender become academically and socially frequent in the national discourse, prominent
researchers continued to explore these concepts. In 1966, Harry Benjamin published The
Transsexual Phenomenon, wherein he touches upon the topics of transfeminine and
transmasculine identity, legal aspects of “transvestism”, and surgical and non-surgical treatments
for transgender patients (Benjamin, 1966). John Money, a critical figure developing the concept
of gender, also published his text Sex Reassignment as Related to Hermaphroditism and
Transsexxualism, wherein he further expounded upon the idea of gender identity and gender
reassignment albeit for conservative purposes of maintaining the gender binary amongst
hermaphroditic patients (Money, 1969). Money’s publication served to draw a dividing line
between intersex patients and morphologically normal patients who were gender non-
TRANSGENDER PERCEPTIONS OF HEALTHCARE 24

conforming, but his position was not necessarily supportive of sex reassignment as a treatment
for transgender patients (Money, 1969).
As research on the concept of gender and transsexuality unfolded over the following
decades, the term “transsexualism” was incorporated into the Diagnostic and Statistical Manual
of Mental Disorders (DSM) under a section coined “Gender Identity Disorders”, which
promoted medical and societal stigma towards transgender people (American Psychiatric
Association, 1987). As transgender medical care evolution is codified in the subsequent
publications of the DSM, with the 2013 publication of the manual removing references to gender
identity disorder and adding the term “gender dysphoria” to focus diagnoses on distress
experienced by transgender patients rather than on the patients themselves (American Psychiatric
Association, 2021). The 1990s represented the first use of stepwise, reversible puberty-blocking
hormones for transgender youth, while the 2000s have formalized gender dysphoria treatments
through seminal medical journal publications as well as a proliferation of transgender treatment
centers that were previously considered a medical rarity (Warwick & Shumer, 2021). While
transgender medicine has become increasingly available since the 1990s, the novelty of
treatments, access to care, public sentiment, insurance coverages, and changing national
leadership among other facets of modern medicine and society have all impacted how the
transgender community interacts with the medical community.
Underlying Factors of Transgender Healthcare Engagement
Patient Sentiment and Medical Mistrust
Medical mistrust is rooted primarily in studies of healthcare perceptions from people of
color who have historically had negative interactions with the medical community (Jaiswal,
TRANSGENDER PERCEPTIONS OF HEALTHCARE 25

2019). Historical trauma, especially the Tuskegee Syphilis study, is a primary driver of medical
mistrust, which has resulted in the study of medical mistrust focusing primarily on Black
communities with findings demonstrating a negative impact on healthcare utilization (Jaiswal,
2019). Concepts of medical mistrust have increasingly expanded to encompass other
marginalized communities’ histories of medical trauma and the subsequent impact on healthcare
utilization. The concept of medical mistrust does not have a universally accepted definition, but
in the context of transgender medicine, mistrust is born out of real or anticipated experiences of
discriminatory treatment which in turn inhibits transgender patients from seeking out healthcare
(D'Avanzo et al., 2019). A study of 21 focus groups of trans women found that participants often
avoided medical settings or refused to seek out necessary healthcare treatment due to prior
experiences of transphobia with providers, staff, and other patients (Sevelius et al., 2016). A
2015 study of 119 transgender and gender non-binary people found that negative healthcare
experiences are most commonly rooted in transphobia, patient misgendering, and a lack of
experience or information regarding transgender patient care (Baldwin et al., 2018). The same
study found that positive patient experiences were rooted in language that respects gender
diversity, experience with transgender patient care, and treating gender identity disclosure as a
routine procedure (Baldwin et al., 2018). In a study of 152 self-identified transgender adults
across 40 states, 71% of participants reported at least one instance of medical mistreatment by
doctors, nurses, emergency medical technicians, or other medical staff (Kosenko et al., 2013).
Despite the increasing prevalence of transgender medical mistrust research in the last
three years and a thorough body of academic work chronicling both positive and negative
transgender patient experiences, existing research specific to medical mistrust and healthcare
utilization is notably sparse. Furthermore, what little research on transgender medical mistrust
TRANSGENDER PERCEPTIONS OF HEALTHCARE 26

that does exist almost exclusively examines how mistrust impacts transgender women’s
perceptions of HIV prevention drugs, namely preexposure prophylaxis (PrEP). Minimal peer-
reviewed studies exist today that examine how transgender patient medical mistrust impacts non-
PrEP healthcare engagement, such as patient post-operative follow-up treatments, hormone
treatment adherence, or routine primary care for transgender patients.
Provider Behaviors and Impact of Educational Experiences
Transgender patient perceptions of discriminatory actions and lack of healthcare
engagement are commonly associated with exposure to perceived problematic provider
behaviors (PPPs), which span a plethora of provider actions such as gender insensitivity
(misgendering), denial of services, substandard care, displays of provider discomfort, verbal
abuse, and even forced patient care (such as admission to psychiatric institutions after a patient
revealed their transgender status) (Kosenko et al., 2013). A survey of 1,253 healthcare providers
found that only 5% of participants were able to accurately answer the survey’s seven knowledge
areas regarding LGBQIA+ patient care and communication, demonstrating a considerable lack of
medical knowledge within the LGBTQIA+ context (Banerjee et al., 2018). Although
LGBTQIA+ patient care knowledge is increasingly well researched among physicians, many
studies obfuscate specific, transgender care insights by aggregating transgender medical care into
a broader research topic of LGBTQIA+ health care. In a transgender-care-specific study of both
transgender patients and heteronormative physicians in Canada, almost all transgender
respondents indicated interactions with physicians who were not aware of transgender care
procedures (McPhail et al., 2016). In the same study, physicians expressed heightened anxiety in
treating transgender patients, not because of their gender identity, but due to a lack of knowledge
and an inability to accurately address the concerns of a transgender patient.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 27

What little research is available documents minimal physician knowledge of transgender
patient care and, in some cases, extremely detrimental perceived problematic provider behaviors.
Fortunately, since 2007 the Association of American Medical Colleges (2007) has recommended
medical schools add curricula related to LGBQIA+ care in hopes that expanded knowledge of
transgender care will improve patient interactions and outcomes and reduce provider stigma.
Despite strides in medical education, there is still significant work to be done as demonstrated by
a 2018 study of 659 medical school students wherein 76.7% indicated they did not feel
adequately competent to care for sexual and gender minorities and 50% indicated their school’s
curriculum did not adequately cover SGM-specific topics (Zelin et al., 2018). A promising
interventional study conducted at the Mayo Clinic’s Alix School of Medicine on both campuses
(Rochester, MN, and Scottsdale, AZ) found that a one hour lecture on gender expression and
sexual orientation increased student knowledge of LGBQIA+ care and promoted favorable
attitudes toward transgender patients for up to one year following the lecture (Najor et al., 2020).
The promise of efficacious transgender curricula and shifting medical school norms and
expectations indicate an increased focus on removing physician stigma and improving
transgender patient interactions, but additional research to bolster the expansion of medical
education curricula (during and after formal medical schooling) remains scant.
Existing Strategies to Improve Transgender Healthcare Engagement
Given well-documented barriers to care and discriminatory interactions that challenge
transgender healthcare utilization, researchers have begun to examine methods for improving
transgender healthcare engagement beyond traditional means such as engaging the trans
community via social media sites (Blotner & Rajunov, 2018). A 2015 study that found increased
contact between heteronormative medical students and their gay and lesbian counterparts
TRANSGENDER PERCEPTIONS OF HEALTHCARE 28

predicted increased favorability toward the LGBTQIA+ community by heterosexual students
(Burke et al., 2015). Burke et al.’s (2015) study was foundational in the development of a
community-based participatory research study that brought the local transgender community and
approximately 33 university physicians together to collaborate on the state of transgender care in
the community (Noonan et al., 2018). This study found that clinicians' feelings of prejudice or
unfamiliarity with the transgender community can be overcome through firsthand interactions
and intergroup contact.
In addition to social media outreach and intergroup contact research, others have begun to
examine barriers that may prevent transgender people from participating in research studies to
further inform the body of academic work related to transgender healthcare. In a series of focus
groups with 28 participants, a study found that participants often feel that research is being
conducted from a cisgender lens, have concerns over privacy or how the research will be used, or
feel that the research is exploitive in nature (Asquith et al., 2021).
National Transgender Medical Infrastructure
Standards of Care and Access to Care

The World Professional Association of Transgender Health (WPATH) has published a
manual titled Standards of Care for the Health of Transsexual, Transgender, and Gender
Nonconforming People since 1979, with the latest publication in 2012 (WPATH, 2012). These
standards of care are intended to serve as global clinical guidelines in the treatment of
transgender patients. The WPATH publication provides clinicians with best practices for topics
such as diagnosing gender dysphoria, mental health considerations, reproductive health, surgery
and postoperative care, therapies, and treatment of children and adolescents with gender
TRANSGENDER PERCEPTIONS OF HEALTHCARE 29

dysphoria. With the emergence of non-binary gender presentation, the standard of care has
shifted from triadic therapy, which was comprised of life experience as a transgender person,
followed by hormone treatment, followed by gender-confirming surgery, to a mental health-
centric model that focuses on assessing, diagnosing, and referring the appropriate treatment for
persistent gender dysphoria (Wylie et al., 2016). Alongside the emergence of improved standards
of care, access to care for transgender patients has similarly expanded despite persistent
challenges in the transgender healthcare landscape.
Since the mid-1960s the United States has seen an expansion in transgender care clinics
and gender identity clinics, but despite modern legislation such as the Affordable Care Act and
increasing federal protections for transgender people, access to care remains challenged for many
transgender people (van Eijk, 2017). In a 2014 publication, Strousma highlighted that Medicaid
and Medicare defined sex reassignment surgery (SRS) as controversial and experimental and use
those considerations as a basis for denying SRS treatment to transgender patients in federal
healthcare programs (Strousma, 2014). The publication identified that SRS was not only well
researched but using claims of controversiality to deny access to medically necessary treatments
endorsed by leading medical associations was inconsistent and outdated. In March 2014, the
Department of Health and Human Services (DHHS) convened a board that determined the
National Coverage Determination precluding SRS as a treatment for gender dysphoria was no
longer reasonable, thus allowing these federal and state programs to cover SRS when deemed
medically necessary (DHHS, 2014). Despite advances in transgender health treatment coverages
and access to clinics and clinicians, a 2017 field study of a US university-based treatment center
found that to provide transgender care, clinicians must also be able to practice health insurance
(van Eijk, 2017). This field study found that without additional labor from clinicians and
TRANSGENDER PERCEPTIONS OF HEALTHCARE 30

administrative staff, transgender patients were challenged in navigating the complex insurance
system and securing payment for and delivery of their medically necessary treatments.
Treatment Options
Modern transgender medical care takes a mental health approach to treat distress
resulting from gender dysphoria, with the role of mental health professionals becoming
increasingly consultative and interdisciplinary across fields of medicine (Hopwood, 2018).
Following a diagnosis of persistent gender dysphoria, mental health, and other clinical care
providers will collaborate and employ WPATH standards to develop a course of treatment(s) to
best meet the needs of the patient (Wylie et al., 2016). These treatments may consist of primary
care, endocrine and hormone therapy, mental health counseling, speech therapy, cosmetic hair
removal, occupational therapy, surgery, and more.
Endocrinologists work with transgender patients and their team of providers to balance
medical risks against desired goals and outcomes during the development of an individual
hormone therapy program (Firek & Sawan-Garcia, 2018). Striking the balance between serving
the needs of transgender patients and mitigating the medical risks of hormone therapy comes
with experience, which is why the U.S. Veterans Health Administration recently initiated a peer-
to-peer training program for physicians treating transgender patients in hormone therapy (Firek
& Sawan-Garcia, 2018). Another evidence-based approach to treating gender dysphoria distress
is Dialectical Behavior Therapy (DBT), which is the premier medical treatment for suicidality
and emotional dysregulation by coaching patients through the development of new and effective
emotional regulation strategies and techniques (Sloan & Berke, 2018). Beyond behavioral and
hormone therapy, some transgender patients may wish to seek gender-affirming surgery, but not
all transgender patients will do so (Schechter & Schechter, 2018). WPATH Standards of Care lay
TRANSGENDER PERCEPTIONS OF HEALTHCARE 31

out clear guidelines by surgery type for the use of gender-affirming surgery as a treatment for
gender dysphoria, so mental health providers, surgeons, and transgender patients must work
together to identify and document if surgery is an appropriate treatment recommendation
(Schechter & Schechter, 2018).
Conceptual Framework
Testa et al.’s (2015) Gender Minority Stress and Resilience Model offers a theory
through which to understand how distal and proximal stressors impact gender minority health
while also accounting for community connectedness and pride as protective resilience factors
that mitigate these stressors. Distal stress factors outlined by Hendricks and Testa (2012) can be
thought of as the external, environmental actions and events that impact transgender and gender-
nonconforming people. Proximal stress factors are internalized factors such as internal
transphobia, negative expectations, and gender identity concealment that impact transgender and
gender nonconforming people’s health outcomes (Hendricks & Testa, 2012). Testa et al.’s
(2015) Gender Minority Stress and Resilience model accounts for distal and proximal stressors
and posits that resilience factors serve to mitigate the negative impact of these stressors on health
outcomes. By transposing distal and proximal minority stressors into the integrative behavior
model, a conceptual framework to critically examine how gender minority stress impacts
primary care utilization can be developed. This conceptual framework also identifies mitigating
resilience factors of community connectedness and pride, as set forth by Testa et al. (2015) as
show in Figure 1, below.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 32

Figure 1
Sexual and Gender Minority Primary Care Utilization Framework

Note. The framework is based on Testa et al.’s (2015) Gender Minority Stress and Resilience
Model.

Past Research Leveraging Theoretical Frameworks
Minority Stress Theory
Testa et al. (2015) are largely credited with the expansion of the minority stress model
(Meyer, 2003) to create the gender minority stress and resilience (GMSR) model for transgender
and gender-nonconforming people. Since the creation of the gender minority stress model,
Testa’s concepts have been further expanded upon by researchers highlighting minority stressors
and protective factors and calling for continued research using trans-specific minority stressors
rather than conducting research through a lens of cisnormativity (Tan et al., 2020). Testa, et al.
(2017) used the GMSR model to examine the phenomenon of high rates of suicidality in a
sample of 816 transgender and gender non-conforming participants. The study found that gender
minority stressors of negative expectations and internalized transphobia are associated with
TRANSGENDER PERCEPTIONS OF HEALTHCARE 33

factors within the interpersonal-psychological theory of suicide that are determinants of suicidal
ideation. To continue understanding health disparities for gender non-conforming people, a study
employed the GSMR to examine the health disparities of 3,568 students (892 of which were
transgender and 892 of which were genderqueer) and found that transgender and genderqueer
students experience more stressors and worse health outcomes than their cisgender counterparts
(Lefevor e al., 2019). The study also found that genderqueer people experienced worse stressors
and health outcomes than transgender women and men who expressed a binary gender. The
GSMR model has also been expanded to examine military-specific gender minority stressors for
transgender veterans, with a study finding that minority stressors of military punishment and
investigations related to gender identity were related to increased suicidal ideation (Tucker et al.,
2019).
Of note, minimal research exists today that critically examines how gender minority
stressors impact transgender and gender-nonconforming healthcare utilization and engagement.
One such study was conducted amongst a sample of 126 transgender sex workers in Shenyang,
China examining the impact of variables related to minority stress such as victimization and
gender non-affirmation on mental health service utilization by transgender women (She et al.,
2021). The study found that mental health service utilization among transgender sex workers was
approximately 33% lower than among Chinese adults with similar mental disorders. She et al.’s
(2021) work is the only study to date that examines how gender minority stress and resilience
impact mental health utilization and shines a light on the dearth of research exploring how
gender minority stress impacts all types of medical care utilization.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 34

Summary
The literature available indicates a clear trend of negative health outcomes for
transgender people, informed by a history of legislative oppression, medical mistrust, and
discrimination and violence. Since the 1940s the concept of transgender health care has slowly
evolved to increasingly provide comprehensive, safe, well-researched treatment options to the
transgender community. Despite advances in healthcare, medical mistrust remains high amongst
the transgender community, which ultimately impacts healthcare utilization. In addition to
medical mistrust, the national infrastructure for the treatment of transgender patients has yet to
mature to a point that safe and equitable access to healthcare can be achieved by transgender
U.S. citizens. In response to the challenges facing transgender healthcare, theoretical frameworks
such as the Gender Minority Stress and Resilience model have evolved. This model equips
researchers and care providers with the tools to better understand how to foster healthcare
utilization and improve patient care quality for transgender and gender-nonconforming people.
These frameworks are particularly salient to this study’s purpose of examining which factors
predict transgender patient healthcare utilization by providing a clear set of predictors for
transgender patient healthcare behavioral intentions.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 35

CHAPTER THREE: METHODOLOGY
This chapter outlines the quantitative methods for collecting data to support the
exploration of the study’s research questions and outlines the data analysis techniques used to
determine the quantitative relationship between components of the conceptual framework in
Chapter 2, see Figure 1, and transgender patient utilization of primary care. Additionally, this
chapter identifies steps taken to ensure reliability, validity, and ethical considerations within all
aspects of instrumentation, data collection, analysis, and discussion of findings.
The purpose of the study is to examine how GMSR explanatory variables shape
transgender patient behaviors and perceptions related to primary healthcare utilization.
Specifically, this study aims to examine what health seeking behaviors look like for transgender
patients, how transgender patients describe their health seeking experiences, what the
relationships between GMSR variables are, and which GMSR factors, if any, predict transgender
patient attitudes and intentions toward primary care utilization. Recommendations for policy and
practice are generated to address the challenge of fostering transgender engagement in primary
care services and reducing transgender healthcare avoidance due to negative healthcare
experiences.
Research Questions
The research questions to be addressed within this study are:
1) What does health-seeking behavior look like for transgender patients seeking primary
health care services?
2) How do transgender patients seeking primary health care services describe their
experience in relation to distal stressors, proximal stressors, and resilience factors?
TRANSGENDER PERCEPTIONS OF HEALTHCARE 36

3) What is the relationship between distal stress factors (gender-related discrimination,
gender-related rejection, gender-related victimization, and non-affirmation of gender
identity), proximal stress factors (internalized transphobia, negative expectations, and
concealment), and resilience factors (community connectedness and pride)?
4) To what extent do distal stress factors (gender-related discrimination, gender-related
rejection, gender-related victimization, and non-affirmation of gender identity),
proximal stress factors (internalized transphobia, negative expectations, and
concealment), and resilience factors (community connectedness and pride) predict
healthcare behaviors and perceptions?
Overview of Design
A quantitative cross-sectional survey instrument was utilized to determine the extent to
which independent variables can predict transgender primary care utilization. An open-ended
survey item asked transgender patients seeking primary healthcare services to describe their
experience, including factors that promote or reduce health-seeking behaviors. Given the aim of
understanding the predictive and descriptive nature of numerous gender and minority stress
factors and healthcare seeking behaviors and attitudes, a quantitative approach for this study is
most appropriate (Creswell & Creswell, 2018). The survey instrument was adapted from the
Gender and Minority Stress and Resilience (GSMR) Measure to collect data on distal and
proximal stressors, resilience factors, and transgender primary care utilization behavioral
intentions (Testa et al., 2015). Data collection via a survey will serve as the quantitative method
for this study, with recruitment occurring primarily via online listservs and social media posts.
Due to anticipated challenges regarding survey participant recruitment, nonprobability sampling
was employed to identify transgender participants based on their availability and convenience
TRANSGENDER PERCEPTIONS OF HEALTHCARE 37

(Creswell & Creswell, 2018). During the recruitment phase, survey participants were screened to
ensure they were 18 years or older and that they identify as a gender other than the gender
initially assigned to them at birth. As identified in Table 1 below, all research questions for this
study were examined using the survey instrument but will be answered through various
analytical methods.
Table 1
Study Data Sources and Analytical Methods
Research Questions Survey
Methods
RQ1: What does health seeking behavior look like for
transgender patients seeking primary health care
services?
X
Descriptive analysis and
Analysis of Variance
(ANOVA) in SPSS v28.
RQ2: How do transgender patients seeking primary
health care services describe their experience in
relation to distal stressors, proximal stressors, and
resilience factors?
X
Content analysis for qualitative
inputs to the open-ended
survey item.
RQ3: What is the relationship between distal stress
factors (gender-related discrimination, gender-related
rejection, gender-related victimization, and non-
affirmation of gender identity), proximal stress factors
(internalized transphobia, negative expectations, and
concealment), and resilience factors (community
connectedness and pride)?
X
Pearson-product moment
correlation testing and
scatterplot generation in SPS
v28.
RQ4: To what extent do distal stress factors (gender-
related discrimination, gender-related rejection,
gender-related victimization, and non-affirmation of
gender identity), proximal stress factors (internalized
transphobia, negative expectations, and concealment),
and resilience factors (community connectedness and
pride) predict healthcare behaviors and perceptions?
X
Multivariate, stepwise
regression testing in SPSS v28.
Research Setting
The study site for recruitment and the survey are not relegated to one geo-location and
were administered in a primarily virtual setting. The recruitment strategy centered on engaging
TRANSGENDER PERCEPTIONS OF HEALTHCARE 38

transgender individuals on social media sites (including but not limited to: Reddit, Facebook,
Instagram, etc.) and using readily available mailing lists or contact listservs. Other studies of
transgender behavioral health intentions and behaviors recruit participants using social media
outreach, which is why this technique was replicated in this study (Miner et al., 2012).
The participants selected for the study were over the age of 18, able to consent to
participate in the survey, and will have a gender identity that differs from the gender identity
assigned to them at birth. Transgender individuals will be screened via a Qualtrics survey link
that is accessible from an unpaid, posted advertisement. The screener survey will identify
qualified transgender participants prior to participating in the study to ensure that responses and
inputs to the GMSR adapted survey instrument support the development of a meaningful
quantitative model.
The Researcher
As the researcher, I am a White, cisgender, homosexual man currently residing in a mid-
size city in Minnesota that homes the flagship medical campuses of the Mayo Clinic. My
relationship to the setting and participants ranges from friendships and acquaintances through a
local volunteering organization to no relationship at all for transgender study participants
recruited through social media outreach. Personal experiences and interactions with transgender
and gender non-conforming people inform my assumptions about negative healthcare
experiences, low expectancies for gender affirming healthcare practices by transgender patients,
and behavioral intentions of primary care utilization. Assumptions about negative healthcare
experiences may impact the weighting and analysis of certain proximal and distal stressors,
therefore a stepwise regression may be called for to comprehensively examine proposed
independent variables and to develop a thorough and accurate model. Additionally, unconscious
TRANSGENDER PERCEPTIONS OF HEALTHCARE 39

biases associated with the researcher’s ethnicity and socioeconomic status may influence the
development and analysis of questions that fail to consider or account for points of view of
people of color and perspectives of individuals from varying socio-economic backgrounds. The
researcher leverages pre-existing research with the GSMR to benchmark variable weighting and
survey development to mitigate potential unconscious biases (Testa, et al., 2017; Sutter, 2017).
Data Sources
This study employed a cross-sectional survey with one open-ended question, see
Appendix B, to collect data from transgender participants and is based on previously developed
survey instruments (Testa, et al., 2017; Sutter, 2017). The aim of this survey is to collect data
pertinent to developing a model for predicting primary care utilization for transgender patients.
Survey
Participants
Conducting research with the transgender population poses challenges to site-based
research due to a lack of significantly concentrated transgender people to support statistical
analysis. Miner et al. (2012) discusses the power of using the internet to improve transgender
research by virtually connecting with a previously inaccessible population. Due to anticipated
challenges in recruiting survey participants, nonprobability sampling was employed to identify
transgender participants based on their availability and convenience (Creswell & Creswell,
2018). Using an online calculator software for multiple regression analysis and factoring for a p-
value of .05, a beta value of .20, an estimated medium effects size (.15), and a total of 10
predictor variables (9 GSMR variables), a sample size of 113 participants was deemed
TRANSGENDER PERCEPTIONS OF HEALTHCARE 40

appropriate for this study (Soper, 2022; Testa et al., 2015). Accounting for data removal of null
responses, incomplete responses, and outliers, a total of 213 responses were collected.
Dummy coding was used for gender (transman, transwoman, and gender non-
conforming), race, and income. Respondents were overwhelmingly transwomen (67%), but
transmen (19%) and gender non-conforming (14%) respondents were also represented.
Additionally, respondents were primarily white (80%), 20-29 (47%), and covered by insurance
(96%). Income responses were used to create two new variables (“>39K per year” and “<39K
per year”) to reduce the overall number of explanatory variables and improve the power of future
regression models. 33% of respondents made less than $39,000 per year, 58% made greater than
$39,000 per year, and 9% opted not to respond. An overview of the survey sample can be seen
below in Table 2.
Recruitment Strategy
The recruitment strategy centered on engaging transgender individuals on social media
sites (Reddit, Facebook, Instagram, etc.) and using readily available mailing lists or contact
listservs. The advertisements were uniform in all posts and were established as a PDF flier with
QR codes and links to access the surveys. Survey participants were screened via a feeder survey
to ensure they are 18 years or older, based in the U.S., and that they identify as a gender other
than the gender initially assigned to them at birth. According to The Williams Institute at the
UCLA School of Law, the transgender community racial makeup is approximately 55% White,
16% Black, 21% Hispanic, and 8% Other (Flores et al., 2016). As you can see in Table 2, this
study’s population was approximately 80% White, 6% Black, 5% Hispanic, and 5% Other which
may have resulted from exclusive online recruiting and online recruitment site selection.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 41

Table 2
Survey Sample Characteristics
Item Type Qty % Item Type Qty %
Gender Transwoman
Transman
Gender Non-Conform.
Prefer not to answer
Total
142
40
30
1
213
67
19
14
0
100
Insurance
Coverage
within Past
12 Months
Yes
No
Prefer not to answer
Total
204
9
0
213
96
4
0
100
Ethnicity Asian
Black
Caucasian
Hispanic/Latinx
Pacific Islander
Other
Prefer not to answer
Total
4
13
171
10
1
8
6
213
2
6
80
5
0
4
3
100
Financial
Status
$0-$10K
$10-$19K
$20-$29K
$30-$39K
$40-$49K
$50-$59K
$60-$69K
$70-$79K
$80-$89K
$90-$99K
$100K+
Prefer not to answer
Total
16
14
16
24
12
14
11
13
9
8
57
19
213
8
7
8
11
6
7
5
6
4
4
27
9
100
Age 18-19
20-29
30-39
40-49
50-59
60-69
70+
Prefer not to answer
Total
19
101
66
13
9
5
0
0
213
9
47
31
6
4
2
0
0
100

Instrumentation
One mixed methods survey instrument with three distinct components was employed
within this study. The first component of the survey, found in Appendix B, served as a screener,
and was only used to collect data necessary to confirm (1) that participants identify as a gender
different to the one originally assigned to them at birth, (2) that they are over the age of 18, and
(3) that they are in the United States. The survey used free text entry, drop-downs, and multiple-
choice questions.
The second component of the mixed methods survey included an informed consent
question, demographic questions, questions regarding gender minority stress and resilience
TRANSGENDER PERCEPTIONS OF HEALTHCARE 42

(Testa et al., 2015) and behavioral intentions and actions of primary care utilization (Sutter,
2017). The second component of the mixed methods survey was 67 questions (including
demographic questions). The total mixed methods survey (inclusive of the first and second
component outlined above) took an average of 21:02 minutes to complete and consisted of nine
GMSR subscales (see Appendix B for weighting criteria). The mixed methods survey was
divided into the following sections: (1) Informed Consent and Discussion of Anonymity, (2)
Demographic Information, (3) Questions related to GSMR Distal Stressors, (4) Questions related
to GSMR Proximal Stressors, (5) Questions related to Gender and Minority Resilience Factors,
and (6) Questions related to Primary Care Utilization. This mixed methods survey is designed to
address research questions one, two, three, and four.
The second survey took approximately 3 minutes to complete and asked survey
participants if they are willing to participate in a follow up interview. Data remain confidential
and are not attributional. Both surveys can be found in Appendix B.
Testa et al.’s (2015) Gender Minority Stress and Resilience Model used two statistical
approaches to evaluate the psychometric properties of the nine scales contained within the
model. The first method consisted of using least square estimators for ordinal survey items and,
after testing for model fit, the researchers conducted a confirmatory factor analysis, which
ultimately supported the nine scale model (Testa et al., 2015). The researchers used SPSS 20 in
their second method to explore construct and criterion validity (Testa et al., 2015). Statistical
analysis found a 𝝌 2 (1559)=5992.04, p < .001, a comparative fit index (CFI) of .93, and root
mean square of error approximation (RMSEA) of .06 (Testa et al., 2015). Additionally,
researchers calculated the following Cronbach’s alpha for each scale below.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 43

Table 3
Internal Consistency Measures for Gender Minority Stress and Resilience Scales
Scale Cronbach’s Alpha
Gender-related Discrimination .61
Gender-related Rejection .71
Gender-related Victimization .77
Non-affirmation of Gender Identity .93
Internalized Transphobia .91
Pride .90
Negative Expectations for the Future .89
Non-disclosure (Concealment) .80
Community Connectedness .90
Note. Table derived from Testa et al. (2015) statistical findings.
The constructs that will be used throughout this study are derived from Testa et. al’s (2017)
Gender and Minority Stress Model. Each construct is defined in Table 4, below.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 44

Table 4
Definitions of Testa et al.’s (2015) Gender Minority Stress Model Variables
Var.
Type
Construct Definition
IV
Gender-related
Discrimination
Difficulty receiving healthcare, documentation, housing, or employment due to
gender-based discrimination.
IV
Gender-related
Rejection
Rejection from a religious, ethnic/racial, social, work, family, or school
communities due to gender identity/expression
IV
Gender-related
Victimization
Past instances of verbal/physical harassment and assault, threats or blackmail,
and property damage due to gender identity/expression
IV
Non-affirmation of
Gender Identity
Experiences related to misgendering, difficulty being perceived as the
appropriate gender, and/or changing behavior to affirm gender
IV
Internalized
Transphobia
Feelings of internal resentment or depression/unhappiness when thinking of
one’s gender identity
IV Pride
Feelings of uniqueness or special-ness related to being transgender or gender
non-conforming
IV
Negative Expectations
for the Future
Expectations that one might struggle with acceptance, employment,
perceptions of being ‘crazy’, or fear of crime/harassment/violence
IV
Non-disclosure
(Concealment)
Efforts taken by respondents to avoid expressing their gender identity (e.g.,
changing voice or the way one walks)
IV
Community
Connectedness
Feelings of closeness/support or isolation from one’s community, friends, or
social support
DV
Health-Seeking
Behavior
# of times seeking, not necessarily receiving, primary healthcare within the last
year.
DV
Health-Receiving
Behavior
# of times receiving, not necessarily seeking, primary healthcare within the last
year.
DV
Perceived Physical
Health
An individual’s feelings about their physical health (1=least healthy and
10=most healthy).
DV
Perceived Mental
Health
An individual’s feelings about their mental health (1=least healthy and
10=most healthy).
Data Collection Procedures
Chapters 1 to 3 of this dissertation were provided to the University of Southern California
Institutional Review Board along with accompanying documentation (IRB protocol, survey
questionnaire, consent template, etc.) via the USC iSTAR portal as an exempt study. The USC
TRANSGENDER PERCEPTIONS OF HEALTHCARE 45

IRB reviewed and approved the study to proceed with data collection and analysis. Following
IRB approval, advertisements posted from a university-based email account, clearly outlined that
to be qualified, participants must be based in the U.S., over the age of 18, and identify as a
gender other than the gender initially assigned at birth. Advertisements were sent via social
media postings, direct email, and forum posts. See Appendix A for Social Media Recruitment
Plan and Fliers. Once participants clicked on the link in the advertisement, they were taken to the
screener component of the survey to determine their qualifications for participation. If
participants were determined unqualified by the screener survey in Qualtrics, a note thanking
them for their time and indicating their inability to participate in the survey was shown.
Participants who are deemed qualified to participate in the study were navigated to the
second component of the survey via Qualtrics logic (outlined above). Participants read through
informed consent and anonymity disclosures and indicated their acceptance of the terms of the
survey. After consenting to the survey, participants were directed to questions related to their
demographic data, and then finally participants were sent through the remaining sections of the
survey to collect study data related to GSMR stressors and determinants of behaviors as they
relate to primary care utilization. Once complete with all mandatory fields and sections of the
first survey, participants were directed to a second, distinct, and final Qualtrics survey. This
second survey was used to gather interest and consent to be contacted for a follow up interview.
The second survey was maintained as a standalone survey to keep identifying information (i.e.
email address) separate from the GMSR and healthcare response data of the first survey.
The survey was conducted via Qualtrics and was capable of being completed on a
personal or public computer or individual mobile phone to increase the number of potential
participants who may have limited access to landline or Wi-Fi internet services. The data set was
TRANSGENDER PERCEPTIONS OF HEALTHCARE 46

scrubbed to ensure no personally identifiable information was present before being uploaded to a
cloud-based database for centralized data storage and management throughout the data analysis
phase of the study. The cloud-based database employed multi-factor authentication to ensure
secure storage of all survey responses.
Data Analysis
Individual responses to survey questions were scored according to Testa et al.’s (2015)
original GMSR model and scoring criteria to generate a single combined score for each
explanatory variable (Discrimination, Victimization, Rejection, Non-Affirmation, Concealment,
Internalized Transphobia, Negative Expectations, Community Connectedness, and Pride).
Normality testing was conducted in SPSS for all 215 records for the following dependent
variables: Healthcare Sought, Healthcare Received, Perceived Physical Health, and Perceived
Mental Health. From the 215 responses, one record was removed to meet normality assumptions
(healthcare_received variable=75) and one record was removed due to influence on regression
models (race=American Indian) which was caused by only one record of its type (i.e., only one
respondent identified as American Indian resulting in erroneous predictive power of that racial
type).
For Research Question #1, descriptive statistics, and analysis of variance (ANOVA) were
run for all demographic factors as well as healthcare seeking, healthcare reception, perceived
mental health, perceived physical health, and healthcare reception modality (e.g., virtual,
emergency room, in person appointment, etc.). For Research Question #2, one open-ended
survey item invited participants to expand on their experiences that informed any responses to
the preceding questions, all of which were aligned to one of the nine explanatory variables within
the GMSR model. Content analysis was used, and each verbatim open-ended response was
TRANSGENDER PERCEPTIONS OF HEALTHCARE 47

reviewed using a list of a prior code (one for each GMSR variable). Several themes (e.g.,
financial insecurity) became evident and all responses were re-analyzed using open coding to
identify themes that were uniquely different from the GMSR variable a priori codes. In total, 55
comments were collected, and 42 comments were utilized in analysis (13 excluded due lack of
clarity or completeness). For Research Question #3, a Pearson-Product moment correlation
matrix was generated, statistically significant relationships were identified, and scatterplots were
generated for each statistically significant relationship.
For Research Question #4, a stepwise regression analysis examining which exogenous
variables (i.e., proximal and distal stressors) best predict each intermediary, endogenous variable
outcome (i.e., psychosocial determinants of behavior) was conducted to remove unnecessary
variables and develop an acceptable model. After reducing statistically insignificant exogenous
variables from the model, a final path analysis model was developed to support multiple
regression analysis. Structural Equation Modeling (SEM) of the data was considered for this
study, but due to limitations in sample size SEM is inappropriate for analysis of the data,
therefore a multiple regression analysis using the same model (single or multi-group) was used to
examine data and determine findings. SPSS Statistics 28 (or a similar software) was used for all
data analysis (Creswell & Creswell, 2018). Partial survey data was collected and included in the
study so long as a full response to a given subscale or dependent variable was indicated (e.g., if a
respondent provided healthcare sought but not healthcare received, their response was included
in analysis for the healthcare sought variable only). The regression model used in this study can
be seen in Figure 2 below and survey response options, which were normalized, as needed, for
regression analysis, can be found in Appendix B. A full construct map of GMSR variables and
dependent variables can be found in Appendix C.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 48

Figure 2
Multiple Regression Model for Data Analysis

Validity and Reliability
To promote survey reliability, numerous participant recruitment channels (social media,
email listservs, word of mouth, etc.) were used, as shown in Appendix A. To promote validity in
instrumentation, survey items were adopted from previous survey instruments that were proven
to achieve effects of statistical significance in their data analysis (Sutter, 2017; Testa, et al.,
2015). Creswell and Creswell (2018) identify selection as a threat to internal validity because
quantitative studies can often inadvertently recruit participants predisposed to similar outcomes.
While a random sample is not feasible for this survey, recruitment occured across numerous
online sites and forums to achieve the broadest and most representative sample possible.
Ethics
The survey instrument first and foremost screened participants to ensure that they were
able to participate in the study and, immediately following the screener survey, participants read
TRANSGENDER PERCEPTIONS OF HEALTHCARE 49

a statement (See Appendix B) that clearly identifies that their participation is voluntary and that
their data submitted in the survey was completely confidential. This overview also clearly
outlined that confidential survey data would be stored for 12 months in a secure, cloud-based
database. In accordance with guidelines laid out in Creswell and Creswell (2018), the survey
instrument being used was thoroughly reviewed by a university Institutional Review Board and
wasalso be peer-reviewed by a dissertation committee to ensure no harmful or damaging
information was gathered because of survey questions. Finally, all data analysis and findings
were thoroughly reviewed by a dissertation committee to confirm data is ethically compliant and
non-attributional or personally identifiable to any study participants and to assess if aggregation
of data and findings discloses harmful information (Creswell & Creswell, 2018).

TRANSGENDER PERCEPTIONS OF HEALTHCARE 50

CHAPTER FOUR: FINDINGS
This chapter provides an overview of the findings that were generated through survey
data collection and analysis. The chapter is organized by research question and provides key
findings or themes for each research question, supported by graphs, tables, and charts, as needed.
Findings indicate that transgender patients seek healthcare across several modalities (e.g., virtual,
emergency room, nonprofit, etc.) and these behaviors vary across demographic factors (e.g.,
income, insurance coverage, race, etc.). Additionally, many GMSR stressor variables (e.g.,
discrimination, victimization, etc.) were found to be statistically significantly positively
correlated, while GMSR resilience factors were not found to mitigate stressor variables. Finally,
regression analysis leveraging the GMSR model and examining (1) healthcare-seeking
behaviors, (2) healthcare-receiving behaviors, (3) perceived physical health, and (4) perceived
mental health was conducted and generally found that income, age, insurance coverage,
concealment, internalized transphobia, gender non-affirmation, and discrimination are
statistically significant predictors for healthcare behaviors and perceptions of transgender
patients.

Research Question 1 Findings
Responses to questions about healthcare seeking modalities and general demographic
response data were used to answer Research Question #1: What does health seeking behavior
look like for transgender patients seeking primary healthcare services. High level findings
indicate that while the majority of transgender patients seek primary care in-person through
primary care providers and local nonprofit clinics, many also utilize telehealth or virtual means
to seek healthcare. Additionally, health-seeking and health-receiving behaviors and mental and
TRANSGENDER PERCEPTIONS OF HEALTHCARE 51

physical health perceptions differed, albeit perhaps not statistically significantly, across
demographic factors such as race, income, insurance coverage, and gender.
Finding 1: Respondents seek in-person care more than virtual care, but heavily depend on
virtual care options
As seen in Figure 3, below, respondents seek healthcare through multiple modes (i.e., in
person, virtual video, telephonically) and venues (i.e., in person Primary Care, urgent care clinic,
nonprofit clinic, etc.). At least 23% of respondents indicated the use of virtual healthcare, while
28% indicated receiving primary healthcare from someone other than an assigned Primary Care
Provider (emergency department or non-profit care provider).
Figure 3
Breakdown of Healthcare Seeking Modalities

TRANSGENDER PERCEPTIONS OF HEALTHCARE 52

Finding 2: Health-Seeking Behaviors vary across Race, Gender, Income, and Insurance
Coverage
A one-way ANOVA was conducted to determine if the Healthcare Sought was different
for racial groups. Participants were classified into five groups: White (n = 170), Black (n = 13),
Asian (n = 4), Hispanic (n=10), and Other (n = 8). There was homogeneity of variances, as
assessed by Levene's test for equality of variances (p = .544). Healthcare Sought score was not
statistically significantly different between different racial groups, F(5, 204) = .621, p =.684.
A one-way ANOVA was conducted to determine if the Healthcare Received was
different for racial groups. Participants were classified into five groups: White (n = 169), Black
(n = 13), Asian (n = 4), Hispanic (n=10), and Other (n = 8). There was homogeneity of variances,
as assessed by Levene's test for equality of variances (p = .190). Healthcare Received score was
not statistically significantly different between different racial groups, F(5, 203) = .732, p =.600.
Means ± standard deviation for health seeking behaviors are shown in Table 5 below.
Table 5
Mean Scores for Health Seeking Behaviors by Race
Health Behavior
Race
White Black Asian Hispanic Race, Other
M SD M SD M SD M SD M SD
Healthcare Sought 3.6 3.8 3.2 2.0 2.0 2.0 4.9 4.8 2.6 1.8
Healthcare Received 3.4 3.3 2.4 2.0 2.0 2.0 4.0 2.6 2.5 1.9

A one-way ANOVA was conducted to determine if the Perceived Physical Health was
different for racial groups. Participants were classified into five groups: White (n = 159), Black
(n = 13), Asian (n = 3), Hispanic (n=10), and Other (n = 8). There was homogeneity of variances,
as assessed by Levene's test for equality of variances (p = .324). Perceived Physical Health score
TRANSGENDER PERCEPTIONS OF HEALTHCARE 53

was not statistically significantly different between different racial groups, F(5, 191) = 1.452, p
=.208.
A one-way ANOVA was conducted to determine if the Perceived Mental Health was
different for racial groups. Participants were classified into five groups: White (n = 165), Black
(n = 13), Asian (n = 4), Hispanic (n=9), and Other (n = 7). There was homogeneity of variances,
as assessed by Levene's test for equality of variances (p = .742). Perceived Mental Health score
was not statistically significantly different between different racial groups, F(5, 197) = 1.108, p
=.357. Means ± standard deviation for health perceptions are shown in Table 6 below.
Table 6
Mean Scores for Health Perceptions by Race
Health Behavior
Race
White Black Asian Hispanic Race, Other
M SD M SD M SD M SD M SD
Perceived Physical
Health
a
7.1 1.7 7.5 1.6 5.7 1.5 7.0 1.5 7.1 1.6
Perceived Mental
Health
a
5.5 2.3 5.2 2.9 5.3 2.6 4.2 2.3 5.0 3.1
a
Scale for each factor is from 1 (least healthy) to 10 (most healthy).
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
due to COVID was different for racial groups. Participants were classified into five groups:
White (n = 171), Black (n = 13), Asian (n = 4), Hispanic (n=10), and Other (n = 8). There was
homogeneity of variances, as assessed by Levene's test for equality of variances (p = .556).
Number of Times Receiving Care due to COVID was not statistically significantly different
between different racial groups, F(5, 205) = .618, p =.686.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
Virtually via Video was different for racial groups. Participants were classified into five groups:
White (n = 171), Black (n = 13), Asian (n = 4), Hispanic (n=10), and Other (n = 8). There was
TRANSGENDER PERCEPTIONS OF HEALTHCARE 54

homogeneity of variances, as assessed by Levene's test for equality of variances (p = .285). The
Number of Times Receiving Care Virtually via Video was not statistically significantly different
between different racial groups, F(5, 205) = 1.038, p =.397.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
Virtually via Telephone was different for racial groups. Participants were classified into five
groups: White (n = 171), Black (n = 13), Asian (n = 4), Hispanic (n=10), and Other (n = 8). The
assumption of homogeneity of variances was violated, as assessed by Levene's test for equality
of variances (p = .005). A Welch ANOVA for Number of Times Receiving Care Virtually via
Telephone was not able to be conducted due to a lack of variance in response data for at least one
racial group.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
Virtually via In Person Appointments was different for racial groups. Participants were classified
into five groups: White (n = 171), Black (n = 13), Asian (n = 4), Hispanic (n=10), and Other (n =
8). There was homogeneity of variances, as assessed by Levene's test for equality of variances
(p = .133). The Number of Times Receiving Care Virtually via In Person Appointments was not
statistically significantly different between different racial groups, F(5, 205) = .515, p =.765.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
Virtually via Emergency Room was different for racial groups. Participants were classified into
five groups: White (n = 171), Black (n = 13), Asian (n = 4), Hispanic (n=10), and Other (n = 8).
There was homogeneity of variances, as assessed by Levene's test for equality of variances (p =
.393). The Number of Times Receiving Care Virtually via Emergency Room was not statistically
significantly different between different racial groups, F(5, 205) = .583, p =.713.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 55

A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
Virtually via Nonprofit Clinic was different for racial groups. Participants were classified into
five groups: White (n = 171), Black (n = 13), Asian (n = 4), Hispanic (n=10), and Other (n = 8).
The assumption of homogeneity of variances was violated, as assessed by Levene's test for
equality of variances (p < .001). A Welch ANOVA for Number of Times Receiving Care
Virtually via Nonprofit Clinic was not able to be conducted due to a lack of variance in response
data for at least one racial group. Means ± standard deviation for health behavior by modality are
shown in Table 7 below.
Table 7
Mean Scores for Health Seeking Modalities by Race
Health Behavior
Race
White Black Asian Hispanic Race, Other
M SD M SD M SD M SD M SD
# Times due to COVID 0.2 0.6 0.1 0.3 0.4 0.5 0.5 0.6 0.3 0.8
# Times Virtual 0.6 1.4 0.6 0.9 0.0 0.0 1.5 1.2 0.5 0.6
# Times Telephone 0.2 0.6 0.1 0.2 0.0 0.0 0.7 1.1 0.0 0.1
# Times in Person 1.8 2.2 1.8 2.1 0.9 0.3 1.5 2.4 0.9 0.7
# Times ER 0.5 1.5 0.6 1.1 0.0 0.0 1.1 1.7 0.5 0.8
# Times Nonprofit 0.4 1.1 0.0 0.0 0.0 0.0 1.4 2.0 0.0 0.0

A one-way ANOVA was conducted to determine if the Healthcare Sought score was
different for insured and uninsured transgender respondents. Participants were classified into two
groups: Insured (n = 202) and Uninsured (n = 9). There was homogeneity of variances, as
assessed by Levene's test for equality of variances (p = .199). Data is presented as a mean ±
standard deviation. Healthcare Sought score was statistically significantly different between
insured and uninsured respondents, F(1, 209) = 5.579, p < .05. Healthcare Sought score for
TRANSGENDER PERCEPTIONS OF HEALTHCARE 56

uninsured respondents (.8 ± 1.6) was lower than insured respondents (3.7 ± 3.7). Post hoc tests
and analysis were not conducted since there were only two groups (insured and uninsured).
A one-way ANOVA was conducted to determine if the Healthcare Received was
different for insured and uninsured transgender respondents. Participants were classified into two
groups: Insured (n = 201) and Uninsured (n = 9). There was homogeneity of variances, as
assessed by Levene's test for equality of variances (p = .523). Healthcare Received score was not
statistically significantly different between insured and uninsured respondents, F(1, 208) =
3.450, p =.065.
A one-way ANOVA was conducted to determine if the Healthcare Sought score was
different for transgender respondents with an income above or below $39K per year. Participants
were classified into two groups: Above $39K (n = 124) and Below $39K (n = 70). The
assumption of homogeneity of variances was violated, as assessed by Levene's test for equality
of variances (p = .006). Data is presented as a mean ± standard deviation. Healthcare Sought
score was statistically significantly different between both income classes, Welch’s F(1, 99.253)
= 4.405, p < .05. Healthcare Sought score for respondents with income less than $39K per year
(4.3 ± 4.5) was higher than respondents with income greater than $39K per year (3.2 ± 3.1). Post
hoc tests and analysis were not conducted since there were only two groups (Above $39K and
Below $39K).
A one-way ANOVA was conducted to determine if the Healthcare Received score was
different for transgender respondents with an income above or below $39K per year. Participants
were classified into two groups: Above $39K (n = 124) and Below $39K (n = 70). There was
homogeneity of variances, as assessed by Levene's test for equality of variances (p = .056).
TRANSGENDER PERCEPTIONS OF HEALTHCARE 57

Healthcare Received score was not statistically significantly different between both income
classes, F(1, 192) = 5.671, p = .198.
A one-way ANOVA was conducted to determine if the Healthcare Sought score was
different for each gender group. Participants were classified into three groups: Trans man (n =
40), Trans woman (n= 141) and Gender Non-Conforming (n = 30). The assumption of
homogeneity of variances was violated, as assessed by Levene's test for equality of variances
(p = .046). Healthcare Sought score was not statistically significantly different between all
gender groups, Welch’s F(1, 59.839) = .589, p = .558.
A one-way ANOVA was conducted to determine if the Healthcare Received score was
different for each gender group. Participants were classified into three groups: Trans man (n =
40), Trans woman (n= 141) and Gender Non-Conforming (n = 30). There was homogeneity of
variances, as assessed by Levene's test for equality of variances (p = .465). Healthcare Received
score was not statistically significantly different between all gender groups, F(2, 207) = .296, p =
.744. Means ± standard deviation for health seeking behaviors are reported in Table 8 below.
Table 8
Mean Scores for Health Seeking Behaviors by Insurance, Income, and Gender

Health
Behavior
Insurance Annual Income Gender
Without With
Below
$39K
Above
$39K
Trans-
woman
Trans-
man GNC
M SD M SD M SD M SD M SD M SD M SD
Healthcare
Sought
0.8* 1.6 3.7* 3.7 4.3* 4.5 3.2* 3.1 3.5 3.5 3.3 2.9 4.5 5.2
Healthcare
Received
1.4 2.5 3.4 3.1 3.7 3.7 3.1 2.8 3.2 3.0 3.6 3.5 3.5 3.0
*p<.05 **p<.01 ***p<.001 for associated ANOVA analysis.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 58

A one-way ANOVA was conducted to determine if the Perceived Physical Health
score was different for insured and uninsured transgender respondents. Participants were
classified into two groups: Insured (n = 189) and Uninsured (n = 9). There was homogeneity of
variances, as assessed by Levene's test for equality of variances (p = .505). Perceived Physical
Health score was not statistically significantly different between insured and uninsured
respondents, F(1, 196) = .791, p = .375.
A one-way ANOVA was conducted to determine if the Perceived Mental Health was
different for insured and uninsured transgender respondents. Participants were classified into two
groups: Insured (n = 195) and Uninsured (n = 9). There was homogeneity of variances, as
assessed by Levene's test for equality of variances (p = .939). Perceived Mental Health score was
not statistically significantly different between insured and uninsured respondents, F(1, 202) =
2.058, p =.153.
A one-way ANOVA was conducted to determine if the Perceived Physical Health score
was different for transgender respondents with an income above or below $39K per year.
Participants were classified into two groups: Above $39K (n = 114) and Below $39K (n = 66).
The assumption of homogeneity of variances was violated, as assessed by Levene's test for
equality of variances (p = .008). Data is presented as a mean ± standard deviation. Perceived
Physical Health score was statistically significantly different between both income classes,
Welch’s F(1, 105.761) = 9.369, p < .05. Perceived Physical Health score for respondents with
income less than $39K per year (6.6 ± 2.0) was lower than respondents with income greater than
$39K per year (7.3 ± 1.6). Post hoc tests and analysis were not conducted since there were only
two groups (Above $39K and Below $39K).
TRANSGENDER PERCEPTIONS OF HEALTHCARE 59

A one-way ANOVA was conducted to determine if the Perceived Mental Health score
was different for transgender respondents with an income above or below $39K per year.
Participants were classified into two groups: Above $39K (n = 118) and Below $39K (n = 69).
There was homogeneity of variances, as assessed by Levene's test for equality of variances (p =
.756). Data is presented as a mean ± standard deviation. Perceived Mental Health score was
statistically significantly different between both income classes, F(1, 185) = 17.108, p < .001.
Perceived Mental Health score for respondents with income less than $39K per year (4.6 ± 2.3)
was lower than respondents with income greater than $39K per year (5.7 ± 2.3). Post hoc tests
and analysis were not conducted since there were only two groups (Above $39K and Below
$39K).
A one-way ANOVA was conducted to determine if the Perceived Physical Health score
was different for each gender group. Participants were classified into three groups: Trans man (n
= 35), Trans woman (n= 133) and Gender Non-Conforming (n = 30). The assumption of
homogeneity of variances was violated, as assessed by Levene's test for equality of variances
(p < .001). Perceived Physical Health score was not statistically significantly different between
all gender groups, Welch’s F(2, 47.891) = 1.663, p = .200.
A one-way ANOVA was conducted to determine if the Perceived Mental Health score
was different for each gender group. Participants were classified into three groups: Trans man (n
= 39), Trans woman (n= 135) and Gender Non-Conforming (n = 30). There was homogeneity of
variances, as assessed by Levene's test for equality of variances (p = .283). Perceived Mental
Health score was not statistically significantly different between all gender groups, F(2, 201) =
.903, p = .407. Means ± standard deviation for respondent’s health perceptions are reported in
Table 9 below.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 60

Table 9
Mean Scores for Health Perceptions by Insurance, Income, and Gender

Insurance Annual Income Gender
Health
Behavior
Without With Below $39K Above $39K
Trans-
woman
Trans-
man GNC
M SD M SD M SD M SD M SD M SD M SD
Perceived
Physical
Health
a

6.6 2.5 7.1 1.7 6.6* 2.0 7.3* 1.6 7.3 1.3 6.7 2.4 6.6 2.3
Perceived
Mental
Health
a
4.2 2.6 5.4 2.3 4.6*** 2.3 5.7*** 2.3 5.5 2.3 5.1 2.7 4.9 2.3
a
Scale for each factor is from 1 (least healthy) to 10 (most healthy).
*p<.05 **p<.01 ***p<.001 for associated ANOVA analysis.

A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
due to COVID was different for insured and uninsured transgender respondents. Participants
were classified into two groups: Insured (n = 204) and Uninsured (n = 9). There was
homogeneity of variances, as assessed by Levene's test for equality of variances (p = .455).
Number of Times Receiving Care due to COVID was not statistically significantly different
between insured and uninsured respondents, F(1, 211) = .132, p = .717.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
Virtually via Video was different for insured and uninsured transgender respondents. Participants
were classified into two groups: Insured (n = 204) and Uninsured (n = 9). There was
homogeneity of variances, as assessed by Levene's test for equality of variances (p = .373).
Number of Times Receiving Care Virtually via Video was not statistically significantly different
between insured and uninsured respondents, F(1, 211) = .594, p = .442.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
Virtually via Telephone was different for insured and uninsured transgender respondents.
Participants were classified into two groups: Insured (n = 204) and Uninsured (n = 9). There was
TRANSGENDER PERCEPTIONS OF HEALTHCARE 61

homogeneity of variances, as assessed by Levene's test for equality of variances (p = .085).
Number of Times Receiving Care Virtually via Telephone was not statistically significantly
different between insured and uninsured respondents, F(1, 211) = .823, p = .365.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
via In Person Appointment was different for insured and uninsured transgender respondents.
Participants were classified into two groups: Insured (n = 204) and Uninsured (n = 9). The
assumption of homogeneity of variances was violated, as assessed by Levene's test for equality
of variances (p = .016). Data is presented as a mean ± standard deviation. Number of Times
Receiving Care via In Person Appointment was statistically significantly different between
insured and uninsured respondents, Welch’s F(1, 31.023) = 57.730, p < .001. The Number of
Times Receiving Care via In Person Appointment score for uninsured respondents (.2 ± .4) was
lower than insured respondents (1.8 ± 2.1). Post hoc tests and analysis were not conducted since
there were only two groups (insured and uninsured).
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
via Emergency Room was different for insured and uninsured transgender respondents.
Participants were classified into two groups: Insured (n = 204) and Uninsured (n = 9). The
assumption of homogeneity of variances was violated, as assessed by Levene's test for equality
of variances (p < .001). Number of Times Receiving Care via Emergency Room was not
statistically significantly different between insured and uninsured respondents, Welch’s F(1,
8.042) = .545, p = .481.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
via Nonprofit was different for insured and uninsured transgender respondents. Participants were
classified into two groups: Insured (n = 204) and Uninsured (n = 9). There was homogeneity of
TRANSGENDER PERCEPTIONS OF HEALTHCARE 62

variances, as assessed by Levene's test for equality of variances (p = .088). Number of Times
Receiving Care via Nonprofit was not statistically significantly different between insured and
uninsured respondents, F(1, 211) = .716, p = .399.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
due to COVID was different for transgender respondents with an income above or below $39K
per year. Participants were classified into two groups: Above $39K (n = 124) and Below $39K (n
= 70). There was homogeneity of variances, as assessed by Levene's test for equality of variances
(p = .697). The Number of Times Receiving Care due to COVID was not statistically
significantly different between both income classes, F(1, 192) = .010, p = .922.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
Virtually via Video was different for transgender respondents with an income above or below
$39K per year. Participants were classified into two groups: Above $39K (n = 124) and Below
$39K (n = 70). The assumption of homogeneity of variances was violated, as assessed by
Levene's test for equality of variances (p < .001). The Number of Times Receiving Care
Virtually via Video was not statistically significantly different between both income classes,
Welch’s F(1, 104.778) = 3.570, p = .062.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
Virtually via Telephone was different for transgender respondents with an income above or
below $39K per year. Participants were classified into two groups: Above $39K (n = 124) and
Below $39K (n = 70). There was homogeneity of variances, as assessed by Levene's test for
equality of variances (p = .322). The Number of Times Receiving Care Virtually via Telephone
was not statistically significantly different between both income classes, F(1, 192) = .268, p =
.606.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 63

A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
via In Person Appointment was different for transgender respondents with an income above or
below $39K per year. Participants were classified into two groups: Above $39K (n = 124) and
Below $39K (n = 70). There was homogeneity of variances, as assessed by Levene's test for
equality of variances (p = .680). The Number of Times Receiving Care via In Person
Appointment was not statistically significantly different between both income classes, F(1, 192)
= 1.418, p = .235.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
via Emergency Room was different for transgender respondents with an income above or below
$39K per year. Participants were classified into two groups: Above $39K (n = 124) and Below
$39K (n = 70). The assumption of homogeneity of variances was violated, as assessed by
Levene's test for equality of variances (p < .001). Data is presented as a mean ± standard
deviation. The Number of Times Receiving Care via Emergency Room was statistically
significantly different between both income classes, Welch’s F(1, 84.475) = 6.109, p < .05. The
Number of Times Receiving Care via Emergency Room score for respondents with income less
than $39K per year (1.0 ± 2.1) was higher than respondents with income greater than $39K per
year (0.3 ± 0.9). Post hoc tests and analysis were not conducted since there were only two groups
(Above $39K and Below $39K).
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
via Nonprofit was different for transgender respondents with an income above or below $39K
per year. Participants were classified into two groups: Above $39K (n = 124) and Below $39K (n
= 70). There was homogeneity of variances, as assessed by Levene's test for equality of variances
TRANSGENDER PERCEPTIONS OF HEALTHCARE 64

(p = .419). The Number of Times Receiving Care via Nonprofit was not statistically significantly
different between both income classes, F(1, 192) = .171, p = .680.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
due to COVID was different for each gender group. Participants were classified into three
groups: Trans man (n = 40), Trans woman (n= 142) and Gender Non-Conforming (n = 30).
There was homogeneity of variances, as assessed by Levene's test for equality of variances (p =
.266). Number of Times Receiving Care due to COVID was not statistically significantly
different between all gender groups, F(2, 209) = .498, p = .608.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
Virtually via Video was different for each gender group. Participants were classified into three
groups: Trans man (n = 40), Trans woman (n= 142) and Gender Non-Conforming (n = 30).
There was homogeneity of variances, as assessed by Levene's test for equality of variances (p =
.394). Number of Times Receiving Care Virtually via Video was not statistically significantly
different between all gender groups, F(2, 209) = .177, p = .838.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
Virtually via Telephone was different for each gender group. Participants were classified into
three groups: Trans man (n = 40), Trans woman (n= 142) and Gender Non-Conforming (n = 30).
There was homogeneity of variances, as assessed by Levene's test for equality of variances (p =
.071). Number of Times Receiving Care Virtually via Telephone was not statistically
significantly different between all gender groups, F(2, 209) = .590, p = .555.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
via In Person Appointment was different for each gender group. Participants were classified into
three groups: Trans man (n = 40), Trans woman (n= 142) and Gender Non-Conforming (n = 30).
TRANSGENDER PERCEPTIONS OF HEALTHCARE 65

There was homogeneity of variances, as assessed by Levene's test for equality of variances (p =
.369). Number of Times Receiving Care via In Person Appointment was not statistically
significantly different between all gender groups, F(2, 209) = .663, p = .516.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
via Emergency Room was different for each gender group. Participants were classified into three
groups: Trans man (n = 40), Trans woman (n= 142) and Gender Non-Conforming (n = 30). The
assumption of homogeneity of variances was violated, as assessed by Levene's test for equality
of variances (p < .001). Number of Times Receiving Care via Emergency Room was not
statistically significantly different between all gender groups, Welch’s F(2, 55.551) = 1.504, p =
.231.
A one-way ANOVA was conducted to determine if the Number of Times Receiving Care
via Nonprofit was different for each gender group. Participants were classified into three groups:
Trans man (n = 40), Trans woman (n= 142) and Gender Non-Conforming (n = 30). There was
homogeneity of variances, as assessed by Levene's test for equality of variances (p = .389).
Number of Times Receiving Care via Nonprofit was not statistically significantly different
between all gender groups, F(2, 209) = .328, p = .721. Means ± standard deviation for
respondent’s health seeking modalities are reported in Table 10 below.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 66

Table 10
Mean Scores for Health Seeking Modalities by Insurance, Income, and Gender

Health
Behavior
Insurance Annual Income Gender
Without With
Below
$39K
Above
$39K
Trans-
woman
Trans-
man GNC
M SD M SD M SD M SD M SD M SD M SD
# Times
due to
COVID

0.2 0.3 0.2 0.6 0.2 0.5 0.2 0.6 0.3 0.7 0.2 0.4 0.1 0.4
# Times
Virtual

0.3 0.9 0.7 1.3 0.9 1.7 0.5 1.1 0.7 1.4 0.7 1.3 0.5 0.9
# Times
Telephone

0.0 0.0 0.2 0.6 0.2 0.5 0.2 0.7 0.2 0.5 0.1 0.3 0.3 1.1
# Times in
Person w/
PCP

0.2*** 0.4 1.8*** 2.1 1.5 1.9 1.8 2.2 1.8 2.3 1.4 1.7 1.7 1.8
# Times
ER

1.6 4.6 0.5 1.1 1.0* 2.1 0.3* 0.9 0.4 1.0 1.1 2.6 0.6 1.0
# Times
Nonprofit
0.1 0.3 0.4 1.1 0.5 1.1 0.4 1.0 0.4 1.1 0.4 1.1 0.3 0.8
*p<.05 **p<.01 ***p<.001 for associated ANOVA analysis.
Finding 3: Health Seeking Behaviors are influenced by the existence or lack of physician
support
In addition to the behaviors and perceptions identified in Findings 1 and 2 above, open-
ended survey responses highlighted the self-perceived impact of physician support (i.e.,
inclusiveness of primary care providers) as an influencer on health-seeking behaviors. Table 11
offers exemplary responses that cited physician inclusiveness and LGBTQIA+ treatment
specialization as enabling supports, while other responses cited denial of services based on
gender identity as a clear barrier to care.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 67

Table 11
Open-Ended Survey Responses Related to Physician Support
Thematic Finding Example quote
Physician supports
promote health
seeking behaviors
“I am medically transitioning, currently in the early stages of HRT. I
have a local trans support group which has been key in all of this.
Family is supportive, only had one friend who ghosted me over it. Out
to those who are close to me in my private life, not out openly in public
yet. Have switched to a PCP that specializes in LGBTQ patients, and
never disclosed my trans status to prior [my] provider for fear of lack of
proper care.” (Transwoman, age 48)
Physician supports
promote health
seeking behaviors
“I don't have many experiences regarding transphobia because I always
seek out affirming doctors, groups, etc. I don't know if I would ever be
willing to risk exposing myself to transphobia by putting myself into
casual situations with cis people that weren't explicitly trans-friendly.”
(Transman, age 19)
Physician supports
promote health
seeking behaviors
“My response is based off the last two years when I have been out as a
transwoman. Living in New York has shaped these answers and I am
lucky to have my GP be my endocrinologist who specializes in gender
care.” (Transwoman, age 32)
Physician supports
promote health
seeking behaviors
“I can afford health care, and my current health care provider (Kaiser)
made transitioning so easy that I was approved for
surgery/hormones/whatever I wanted after a 30-minute phone
consultation, and their trans service center took care of setting
everything up. I'm on trans easy mode, and I know that it's partly
because I'm white-passing and well-paid and neurotypical and I can
pass as a tomboyish woman. I'm not seen as "threatening" or
"burdensome" like a lot of my trans peers are.” (Gender Non-
Conforming Person, age 39)

TRANSGENDER PERCEPTIONS OF HEALTHCARE 68

Research Question 2 Findings
The open-ended survey item responses were used to answer Research Question #2: How
do transgender patients seeking primary health care services describe their experience in relation
to distal stressors, proximal stressors, and resilience factors? A high-level sentiment analysis
(positive vs. negative comment) was conducted to identify major sentiment themes. Additionally,
a full count of both a priori and open codes was conducted to identify recurring codes (See
Appendix D) for thematic analysis, which follows Table 12. The four themes with the highest
recurrence, as assessed by code count and distinctness (i.e., minimal overlap with other codes)
were selected for deeper analysis: Concealment, Income, Mental Health, and Community
Connectedness. Concealment themes generally seem to indicate a fear for personal safety, while
themes for income are related to financial or employment insecurity due to trans-ness. Mental
health themes were derived from multiple indications of diagnosed mental health illnesses and
comments describing the conflation of gender dysphoria and mental illness, while community
connectedness themes were often related to social and community-based supports.
Table 12
Sentiment Analysis Overview of Open-Ended Survey Responses by Parent Codes
Parent Codes (A Priori)
Comments
Total (Pos/Neg)
Parent Codes (Open Coding)
Comments
Total (Pos/Neg)
Discrimination 3 (0+/3-) Mental Health (Theme 3) 5 (1+/4-)
Victimization 4 (0+/4-) Physical Health 3 (1+/2-)
Non-Affirmation 5 (1+/4-) Income (Theme 2) 5 (1+/4-)
Negative Expectations
a
10 (2+/8-) Presence of Physician Support
c
6 (5+/1-)
Concealment (Theme 1) 13 (1+/12-)

Internalized Transphobia 2 (0+/2-)

Community Connectedness (Theme 4) 9 (8+/1-)
Pride
b
6 (6+/0-)
a
Negative Expectations and Concealment coded comments had significant overlap, therefore Concealment (as the higher
of the two theme counts) will be explored in more detail.
b
Pride and Community Connectedness coded comments had significant overlap, therefore Community Connectedness (as
the higher of the two theme counts) will be explored in more detail.

c
Physician supports discussed as part of RQ#1 and have therefore been excluded from additional thematic analysis as part
of RQ#2.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 69

Theme 4: Concealment as a solution to receiving primary care
A recurring theme among respondents was the concealment of gender identity.
Responses indicated that concealment of gender identity was born out of fear and to facilitate
social interactions (such as seeking healthcare). Some respondents mentioned never being denied
care or a lack of experienced discrimination but credited these outcomes to concealment of their
gender identity. For respondents who cited concealment as a commonly used technique, the
response often included language around fear for personal safety, or fear of being misunderstood.
Of the 42 total responses, 57% (23) were related to concealment. Table 13, below, offers
exemplary responses that demonstrate how concealment is used as an enabler to receive care and
securing psychological and physical safety in some cases but is an inhibitor to primary care in
other cases.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 70

Table 13
Open-Ended Survey Responses Related to Concealment as a Solution to Receiving Primary Care
Thematic Finding Example quote
Concealment
Concealment as an
inhibitor to
healthcare
“I am out in most places, but not work or most of family… I pass in my
voice, but not all the time in person by appearance, although I tend to
not reveal I am trans in online spaces to people who don't know me. I
haven't seen a primary care doctor for years, mostly because I am afraid
of people seeing me there, and them not understanding. My care is
exclusively via an LGBTQIA+ clinic with an informed consent model.”
(Transwoman, age 27)
Concealment due
to fear of personal
safety
“I face less discrimination than a lot of people because I’ve gone to great
lengths to make sure I pass. If people could tell I was trans, I’d have a
much harder time.” (Transwoman, age 24)
Concealment due
to fear of personal
safety
“I answered some questions "never" because I have never attempted
them, but I have genuinely never been denied medical care. I was
closeted to all but close friends for many years and only started to
medically transition in secret a few months ago. I do not currently
express my true gender outside my home, mostly out of fear.”
(Transwoman, age 31)
Concealment due
to fear of personal
safety
“I personally would love if I could be plainly open about my gender
identity with everyone I meet. Unfortunately, I have faced sexual
violence in the past as a direct result of sharing my gender identity, so I
try to avoid it unless I know the person I am speaking to is safe.”
(Transwoman, age 22)

Theme 5: Financial insecurity and employment insecurity are barriers to receiving
primary care
A recurring theme among respondents was financial insecurity and/or employment
insecurity as a barrier to care. Of the 42 total responses, 12% (5) were related to financial
insecurity. Table 14 offers exemplary responses that demonstrate how fear of financial or
employment insecurity contributed to negative mental health outcomes, born from enduring
workplace discrimination, healthcare avoidance due to unaffordable care, and feelings of having
to choose between gender-affirming care and basic needs.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 71

Table 14
Open-Ended Survey Responses Related to Financial Security
Thematic Finding Example quote
Financial Insecurity
Employment
Insecurity
“Other physical and mental health difficulties have exacerbated problems
I've had seeking medical care. The most significant barrier to medical
care, I feel, is financial, largely contributed to by employment insecurity
and inadequate wages.” (Transwoman, age 27)
Employment
Insecurity
“I had to leave a job I loved because the constant misgendering started to
affect my mental health, then I was attacked and threatened by a customer
and they did nothing about it. I didn’t realize how bad that affected me
until this survey. I want to live and be free, I’m proud of being a trans
man, I have no shame, nor do I want to hide it. I just wish it didn’t come
at a cost.” (Transman, age 24)
Employment
Insecurity
“The industry of work I am in is conservative, and I fear (as a
commissioned salesperson) a huge loss of income upon coming out at
work. I struggle a lot with finances and have to choose to put off some
gender-related healthcare because I cannot afford it.” (Transwoman, age
27)
Housing
Insecurity and
Employment
Insecurity
“Didn't realize I was trans until roughly 4 months before my 48th
birthday…I am happy in who I am for the first time in my life, but know
it will draw much hostility and create additional hardships. Main
concerns now are current and future employment, and current and future
housing. I expect my lease will not be renewed once my landlord finds
out, despite being (in his words) "a model tenant" for the last 10 years.”
(Transwoman, age 48)

Theme 6: Conflation of gender identity and mental health illnesses by primary care
providers act as barriers to receiving primary care

A recurring theme amongst respondents was the conflation of mental illness and gender
dysphoria as a barrier to care and perceptions of social stigma and mental health. Respondents
indicated that in some instances physicians seemed to mistakenly attribute mental health
challenges with gender identity. Additionally, some respondents indicated that they felt feelings
of social stigma, born out of their gender identity, and related to their mental health. Of the 42
total responses, 17% (7) were related to concealment. Table 15 offers exemplary responses that
TRANSGENDER PERCEPTIONS OF HEALTHCARE 72

demonstrate respondents’ difficulties in securing gender-affirming care due to conflating gender-
affirming care needs with mental illnesses. Moreover, responses below indicate fear of losing
access to healthcare due to perceived mental unstableness by providers and judgment by society.
Table 15
Open-Ended Survey Responses Related to Gender Identity and Mental Illness
Thematic Finding Example quote
Mental Illness
Conflation of
gender dysphoria
and mental illness
“I have an undiagnosed/misdiagnosed mental health issue. It's
noticeable to other people that something is wrong, and this affects
how people view the validity of my gender identity and mental health
issues. Others (including some medical professionals) keep viewing
the two as connected and keep claiming that I must be mentally ill
because I'm trans or that I only think I'm trans because I'm mentally
ill. It's caused me to avoid seeking treatment for my mental health
and it's continued to get worse as a result.” (Transman, age 19)
Conflation of
gender dysphoria
and mental illness
and social stigma
and mental health
“Feel as I am made out to be a predator/mentally unsound because I
am transgender. Currently medically transitioning but wondering if
care could be taken from me at any moment really does worry me.”
(Transman, age 20)
Social stigma and
perceived mental
health
“I have mixed emotions about my current mental health and body
image… I identify as Non-Conforming, presentably masculine, but
want to live a life where I can dress and appear feminine and not be
judged, without pursuing a full transition.” (Gender Non-Conforming
Person, age 26)
Theme 7: Community Connectedness as a facilitator to seeking primary healthcare
A recurring theme amongst respondents was a sense of community connectedness or
social support that has improved access to healthcare and gender-affirming healthcare
experiences. Of the 42 total responses, 24% (10) were related to community connectedness.
Table 16 offers exemplary responses that demonstrate how respondents felt that living in socially
and politically liberal areas supported their healthcare needs. Additionally, responses below
suggest that even when living in a culturally or politically conservative area, strong social
supports may encourage gender minorities in their health seeking behaviors.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 73

Table 16
Open-Ended Survey Responses Related to Community Connectedness

Thematic Finding Example quote
Community Connectedness
Support of
friends/family/employer
“I have an accepting group of almost entirely cis
friends. I don't mind discussing trans issues and identity
with friends, but I avoid it with just anyone. I don't live
in a politically safe area but have had a great experience
getting healthcare.” (Transwoman, age 39)
Support of
friends/family/employer and
located in a politically liberal
community
“I'm trying to live as visibly queer as I can so that others
will know that we exist, but sometimes it feels very
futile. Honestly, though, I can't complain. I have a
supportive family, an indifferent boss, and good friends.
I live in a liberal city with an active LGBTQ+
community. I've never had to fear for my safety
because of my gender identity.” (Gender Non-
Conforming Person, age 34)
Located in a politically liberal
area
“The region of the US that participants live in might
strongly impact their responses. I recently moved from
the Deep South to the Pacific Northwest, which is a
much more gender affirming area so my experiences
have been vastly different.” (Gender Non-Conforming
Person, age 22)

TRANSGENDER PERCEPTIONS OF HEALTHCARE 74

Research Question 3 Findings
To answer Research Question #3: “What is the relationship between distal stress
factors (gender-related discrimination, gender-related rejection, gender-related victimization, and
non-affirmation of gender identity), proximal stress factors (internalized transphobia, negative
expectations, and concealment), and resilience factors (community connectedness and pride )?” a
Pearson’s product-moment correlation between all explanatory variables of the Gender Minority
Stress and Resilience model was run in SPSS version 28. The Pearson correlation is reported for
all variable combinations in Table 12, below, while relationships and correlation graphs for
statistically significant variable combinations are reported in more detail in Appendix E:
Pearson-Product Moment Q-Q Plots Supporting RQ#3. For relationships that are indicated as not
statistically significant in Table 17 below, additional investigation and reporting was not
conducted.
Table 17
Correlation Matrix for Gender Minority Stress and Resilience Explanatory Variables, n=213

Variable 1 2 3 4 5 6 7 8 9
1. Discrimination -

2. Rejection .59
**
-

3. Victimization .52
**
.57
**
-

4. Non-Affirmation .21
**
.22
**
0.11 -

5. Internalized Transphobia .19
**
.33
**
.24
**
.43
**
-

6. Negative Expectations .39
**
.44
**
.36
**
.54
**
.56
**
-

7. Concealment .23
**
.35
**
.28
**
.39
**
.56
**
.68
**
-

8. Pride 0.00 -0.02 -0.02 .40
**
-0.13 .25
**
.15
*
-

9. Community
Connectedness
-0.01 0.04 0.10 .30
**
-0.07 .28
**
.38
**
.56
**
-
*p<.05 **p<.01 ***p<.001.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 75

Research Question 4 Findings
As indicated in Chapter 3, a multivariate regression model was run with all nine
explanatory variables of the Gender Minority Stress and Resilience Model (seven stressors and
two resilience factors) as well as demographic covariates (race, income, insurance status, and
gender). These analyses were conducted to answer Research Question #4: To what extent do
distal stress factors (gender-related discrimination, gender-related rejection, gender-related
victimization, and non-affirmation of gender identity), proximal stress factors (internalized
transphobia, negative expectations, and concealment), and resilience factors (community
connectedness and pride) predict transgender primary healthcare utilization? These analyses
were conducted utilizing the following dependent variables: (1) Total Number of Times Seeking
Healthcare in the Past Year; (2) Total Number of Times Receiving Healthcare in the Past Year;
(3) Perceived Physical Health; and (4) Perceived Mental Health. The regression analysis for each
dependent variables is discussed in Findings 1 to 4, below.
The findings of the analyses conducted for Research Question #4 broadly indicate that
income, insurance, age predict health-seeking behaviors. Additionally, findings suggest that
insurance coverage, age, and past discrimination experiences predict health-receiving behaviors.
Income, gender non-affirmation, and community connectedness predict for an individual’s
perceived physical health. Finally, internalized transphobia, age, and income all predict an
individual’s perceived mental health.
Finding 8: Transgender healthcare seeking behaviors are moderately predicted by a
gender minority stress and resilience explanatory model

Analysis of the original healthcare sought variable failed assumptions of
homoscedasticity; therefore, a logarithmic transformation was completed, and the analysis was
TRANSGENDER PERCEPTIONS OF HEALTHCARE 76

re-run. There was independence of residuals, as assessed by a Durbin-Watson statistic of 1.85. A
visual inspection of partial plots indicated sufficient linearity between the dependent and
independent variables. The model demonstrated linearity and homoscedasticity, as assessed by
visual inspection of a plot of studentized residuals versus unstandardized predicted values. No
multicollinearity was detected as all Pearson product-moment values fell below the threshold of
.7 and Collinearity Tolerance was above .1 for all observed variables. Analysis of studentized
deleted residuals, leverage values, and Cook’s Distance values indicated no outliers, leverage
points, or influencers. Finally, analysis of the normal P-P plot of the regression standardized
residual met normality assumptions.
A stepwise multiple regression was run to predict healthcare sought from gender, age,
insurance coverage, race, income, discrimination score, gender rejection score, gender
victimization score, gender non-affirmation score, internalized transphobia score, negative
expectations score, concealment score, community connectedness score, and pride score. The
multiple regression model statistically significantly predicted healthcare sought, F(3, 190) =
9.96, p < .001, adj. R
2
= .12. Only insurance coverage, income, and age proved statistically
significantly to the prediction, p < .005. Regression coefficients and standard errors can be found
in Table 18 (below).

TRANSGENDER PERCEPTIONS OF HEALTHCARE 77

Table 18
Multivariate Stepwise Regression for Dependent Variable Healthcare Sought
Healthcare Sought B
95% CI for B
SE B ꞵ R
2
∆R
2

LL UL
Model .14 .12***
Constant -.13 -1.05 .29 .13
Insurance
Coverage
a
.44*** .24 .65 .10 .30***
Income
b
.15** .05 .24 .05 .22**
Age .01** .00 .01 .00 .21**
Note. Model= “Stepwise” method in SPSS Statistics; total N = 194; B= unstandardized regression coefficient; CI =
confidence interval; LL = lower limit; UL = upper limit.; SE B=standard error of coefficient; ꞵ= standardized
coefficient; R
2
=coefficient of determination; ∆R
2
=adjusted R
2
.
a
0 = No insurance coverage, 1 = Insurance coverage.
b
0 = Annual Income >$39K, 1 = Annual Income< $39K.
*p<.05 **p<.01 ***p<.001.

Finding 9: Transgender healthcare receiving behaviors are minimally predicted by a
gender minority stress and resilience explanatory model

Analysis of the original healthcare received variable failed assumptions of normality;
therefore, a logarithmic transformation was completed, and analysis was re-run. There was
independence of residuals, as assessed by a Durbin-Watson statistic of 1.33. A visual inspection
of partial plots indicated sufficient linearity between the dependent and independent variables.
The model demonstrated linearity and homoscedasticity, as assessed by visual inspection of a
plot of studentized residuals versus unstandardized predicted values. No multicollinearity was
detected as all Pearson product-moment values fell below the threshold of .7 and collinearity
tolerance was above .1 for all observed variables. Analysis of studentized deleted residuals,
leverage values, and Cook’s Distance values indicated no outliers, leverage points, or
influencers. Finally, analysis of the normal P-P plot of the regression standardized residual met
normality assumptions.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 78

A stepwise multiple regression was run to predict healthcare received from gender, age,
insurance coverage, race, income, discrimination score, gender rejection score, gender
victimization score, gender non-affirmation score, internalized transphobia score, negative
expectations score, concealment score, community connectedness score, and pride score. The
multiple regression model statistically significantly predicted healthcare received, F(3, 190) =
6.02, p < .001, adj. R
2
= .07. Only insurance coverage, age, and discrimination score proved
statistically significantly to the prediction, p < .05. Regression coefficients and standard errors
can be found in Table 19 (below).
Table 19
Multivariate Stepwise Regression for Dependent Variable Healthcare Received
Healthcare Received B
95% CI for B
SE B ꞵ R
2
∆R
2

LL UL
Model
.09 .07***
Constant
-.03 -1.05 .29 .13
Insurance Coverage
a
.32** .24 .65 .10 .21**
Age

.01** .05 .24 .00 .19**
Discrimination
Score
.03* .00 .01 .02 .15*
Note. Model= “Stepwise” method in SPSS Statistics; total N = 194; B= unstandardized regression coefficient; CI =
confidence interval; LL = lower limit; UL = upper limit.; SE B=standard error of coefficient; ꞵ= standardized
coefficient; R
2
=coefficient of determination; ∆R
2
=adjusted R
2
.
a
0 = No insurance coverage, 1 = Insurance coverage.
*p<.05 **p<.01 ***p<.001.

Finding 10: Transgender perceived physical health is minimally predicted by a gender
minority stress and resilience explanatory model

There was independence of residuals, as assessed by a Durbin-Watson statistic of 1.76. A
visual inspection of partial plots indicated sufficient linearity between the dependent and
independent variables. The model demonstrated linearity and homoscedasticity, as assessed by
visual inspection of a plot of studentized residuals versus unstandardized predicted values. No
TRANSGENDER PERCEPTIONS OF HEALTHCARE 79

multicollinearity was detected as all Pearson product-moment values fell below the threshold of
.7 and Collinearity Tolerance was above .1 for all observed variables. Analysis of studentized
deleted residuals found two records with >3 standard deviations, but further investigation
indicated normal response data therefore the record was included in analysis. Analysis of
leverage values and Cook’s Distance values indicated no leverage points or influencers. Finally,
analysis of the normal P-P plot of the regression standardized residual met normality
assumptions.
A stepwise multiple regression was run to predict perceived physical healthcare from
gender, age, insurance coverage, race, income, discrimination score, gender rejection score,
gender victimization score, gender non-affirmation score, internalized transphobia score,
negative expectations score, concealment score, community connectedness score, and pride
score. The multiple regression model statistically significantly predicted perceived physical
health, F(3, 176) = 7.75, p < .001, adj. R
2
= .10. Only income, gender non-affirmation score, and
community connectedness score proved statistically significantly to the prediction, p < .05.
Regression coefficients and standard errors can be found in Table 20 (below).

TRANSGENDER PERCEPTIONS OF HEALTHCARE 80

Table 20
Multivariate Stepwise Regression for Dependent Variable Perceived Physical Health
Perceived Physical
Health
B
95% CI for B
SE B ꞵ R
2
∆R
2

LL UL
Model
.12 .10***
Constant 7.64*** 7.06 8.22 .29
Income
a
-.80** -1.29 -.31 .25 -.23**
Gender Non-
Affirmation
-.05** -.09 -.02 .02 -.24**
Community
Connectedness
.04* .00 .09 .02 .15*
Note. Model= “Stepwise” method in SPSS Statistics; total N = 180; B= unstandardized regression coefficient; CI =
confidence interval; LL = lower limit; UL = upper limit.; SE B=standard error of coefficient; ꞵ= standardized
coefficient; R
2
=coefficient of determination; ∆R
2
=adjusted R
2
.
a
0 = Annual Income >$39K, 1 = Annual Income< $39K.
*p<.05 **p<.01 ***p<.001.
Finding 11: Transgender perceived mental health is moderately predicted by a gender
minority stress and resilience explanatory model

Analysis of the original healthcare sought variable failed assumptions of
homoscedasticity; therefore, a logarithmic transformation was completed, and analysis was re-
run. There was independence of residuals, as assessed by a Durbin-Watson statistic of 1.88. A
visual inspection of partial plots indicated sufficient linearity between the dependent and
independent variables. The model demonstrated linearity and homoscedasticity, as assessed by
visual inspection of a plot of studentized residuals versus unstandardized predicted values. No
multicollinearity was detected as all Pearson product-moment values fell below the threshold of
.7 and Collinearity Tolerance was above .1 for all observed variables. Analysis of studentized
deleted residuals found one record with >3 standard deviations, but further investigation
TRANSGENDER PERCEPTIONS OF HEALTHCARE 81

indicated normal response data therefore the record was included in analysis. Analysis of
leverage values and Cook’s Distance values indicated no leverage points or influencers. Finally,
analysis of the normal P-P plot of the regression standardized residual met normality
assumptions.
A stepwise multiple regression was run to predict perceived mental health from gender,
age, insurance coverage, race, income, discrimination score, gender rejection score, gender
victimization score, gender non-affirmation score, internalized transphobia score, negative
expectations score, concealment score, community connectedness score, and pride score. The
multiple regression model statistically significantly predicted perceived mental health, F(3, 183)
= 25.90, p < .001, adj. R
2
= .29. Only internalized transphobia score, age, and income proved
statistically significantly to the prediction, p < .001. Regression coefficients and standard errors
can be found in Table 21 (below).
Table 21
Multivariate Stepwise Regression for Dependent Variable Perceived Mental Health
Perceived Mental
Health
B
95% CI for B
SE B ꞵ R
2
∆R
2

LL UL
Model
.30 .29***
Constant
5.34*** 4.19 6.49 .58
Internalized
Transphobia
-.09*** -.12 -.06 .02 -.38***
Age

.05*** .02 .08 .02 .23***
Income
a
-1.01** -1.63 -.39 .31 -.21**
Note. Model= “Stepwise” method in SPSS Statistics; total N = 187; B= unstandardized regression coefficient; CI =
confidence interval; LL = lower limit; UL = upper limit.; SE B=standard error of coefficient; ꞵ= standardized
coefficient; R
2
=coefficient of determination; ∆R
2
=adjusted R
2
.
a
0 = Annual Income >$39K, 1 = Annual Income< $39K.
*p<.05 **p<.01 ***p<.001.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 82

Summary
Findings indicated various modalities and health seeking behaviors across race, gender,
income, and insurance cover as well as the importance of physician supports and community
connectedness in shaping healthcare behaviors. While a full analysis of variance was not
conducted, health seeking behaviors and perceptions differ based on key demographic variables,
suggesting future areas for focused study segmented by factors such as race or income status.
Additionally, open-ended survey responses suggest that gender identity concealment, financial
insecurity, and conflation of mental health and gender dysphoria as inhibitors to care.
Alternatively, open-ended survey responses suggest that community connectedness may
facilitate primary healthcare seeking behaviors. When seeking to examine relationships between
GMSR explanatory variables, statistically significant, positively correlated relationships between
variables exist for many gender minority stressors (e.g., discrimination, victimization, etc.).
Somewhat surprisingly, statistically significant, negative correlations were not found between
gender minority resilience factors (pride and community connectedness) and gender minority
stressors, as suggested by Testa et. al’s (2015) original model. Finally, data analysis suggests that
gender identity concealment, income, and insurance are key drivers in health behaviors, while
discrimination, internalized transphobia, and gender non-affirmation inhibit healthcare behaviors
and diminish perceived health.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 83

CHAPTER FIVE: RECOMMENDATIONS
This chapter consists of a discussion of findings, a high-level mapping of summarized
findings to proposed recommendations, and a detailed explanation of each recommendation
derived from the data analysis conducted to answer the study’s research questions.
Discussion of Findings
Findings for all four research questions highlighted several insights that may inform
recommendations to improve transgender healthcare behavioral intentions, perceived physical
health, and perceived mental health. Survey responses indicated that transgender patient
frequently seek healthcare outside of scheduled appointments with their primary care provider.
Additionally, financial insecurity was a common theme in open-ended survey responses and this
finding was further supported by the predominance of both income and insurance coverage
(potentially a proxy for income) variables predictive power and statistical significance in several
models. Open ended responses and quantitative analysis also found several GMSR variables that
were statistically significant and should be accounted for when developing recommendations.
Table 22 identifies the predominant drivers of transgender healthcare behavioral intentions and
perceptions uncovered by answering each research question and aligns each driver to proposed
recommendations identified in this chapter.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 84

Table 22
Alignment of Gender Minority Drivers of Healthcare Behaviors and Perceptions to Research
Questions and Recommendations
Drivers Summary of Key Related Findings
RQs
Rec.
1 2 3 4
Healthcare
Modality
• Most people seek care in person, but at least 23% of interactions occur
virtually
X 1

Employment
& Income
• Open ended responses cited employment and income as barriers to care
• Lower Income predicts for increased healthcare seeking behaviors
• Lower Income predicts decreased perceived physical health
• Lower Income predicts decreased perceived mental health
X X X 1, 2

Insurance
Coverage
• Uncovered people utilize the ER more than covered people
• Uncovered people receive more care than they intentionally seek
• Insurance coverage predicts higher healthcare reception
X X 2

Community
Connectedness
• Open ended responses cited comm. connectedness as an enabler to care
• Community Connectedness, Pride, Negative Expectations, and
Concealment are positively correlated
• Increased Community Connectedness predicts higher perceived physical
health
X X X 2, 3, 5

Concealment

• Open ended responses cited concealment as a method to achieve
psychological and physical safety
• Community Connectedness and Concealment are positively correlated
X X 3, 5

Physician
Support
• Open ended responses cited affirming providers as enablers to care
• Open ended responses cited conflation of gender identity and mental
illness as an inhibitor to care
• Increased Gender Non-Affirmation predicts lower perceived physical
health
X X X 4

Internalized
Transphobia
• Increased Internalized Transphobia predicts lower perceptions of mental
health
X 4, 5

Discrimination

• Increased Discrimination predicts higher healthcare reception
X 4, 5
Recommendations for Practice
Based on findings from data analysis and the identification of several key drivers, five
total recommendations have been identified to attempt to improve the current state of
transgender healthcare behaviors and perceptions. These recommendations take a multi-pronged
approach by addressing access to healthcare by: (1) establishing a use case for multi-modal, low-
cost primary care for transgender patients; (2) establishing a non-profit and government coalition
TRANSGENDER PERCEPTIONS OF HEALTHCARE 85

centered on improving healthcare access and defending employee rights of transgender people;
(3) incentivizing the Human Right’s Campaign Municipality Equality Index through public
funding; (4) improving the state of provider training as it relates to gender affirming care; and (5)
improving the state of high school counselor skills as it relates to caring for gender minority
youth. Each recommendation is discussed in further detail below.
Recommendation 1: Establish a Use-Case for Low-Cost, Accessible, Multi-Modal
Healthcare
Multi-modal, specifically virtual, healthcare options are a key facet of gender minority
healthcare seeking and reception behaviors. Additionally, the cost of healthcare and the ability to
receive gender-affirming care are contributing factors to healthcare behaviors and perceptions.
Specifically, income is a strong predictive factor for healthcare seeking behaviors, perceived
physical health, and perceived mental health. Additionally, many open-ended responses cited
employment insecurity or financial insecurity as a primary concern for transgender people.
While direct connections between financial insecurity and healthcare behaviors were not made in
open-ended responses, data suggests that income is a critical driver in health behaviors and
perceptions for transgender people. Based on these findings, a low-cost or free telehealth
solution for primary healthcare consultations for gender minorities could improve healthcare-
seeking and perceived mental and physical health. While this solution would be an
unprecedented offering of gender-affirming care within the U.S. healthcare landscape of
federally sponsored health initiatives, it is financially feasible if operated as a centralized
program covered under the Affordable Care Act and operated as a similar construct to the
Substance Abuse and Mental Health Services Administration (SAMHSA).
TRANSGENDER PERCEPTIONS OF HEALTHCARE 86

In practice, this program would manifest as a centrally managed and funded virtual
primary care consultation service that offers video, telephone, and texting services for gender
and sexual minorities to seek primary care services. Similar to SAMHSA, this program would be
funded by a congressionally approved presidential budget and operating under a federal mandate
to improve the health of U.S. citizens (SAMHSA, 2022). Current SAMHSA funding is
approximately, $9.6 billion and has been increasing substantially year over year (SAMHSA,
2022). Unlike SAMHSA, this program would be focused exclusively on providing virtual
primary care and mental health resources for the transgender community. In 2020, SAMHSA
funding per patient treated was approximately $8,790 USD per person treated with over one
million people helped or treated for substance abuse or mental health issues (SAMHSA, 2020).
While the true costs of providing a virtual method for transgender patients to engage primary
care physicians is not well understood, the success of the SAMHSA program suggests that a
similar model may be adopted successfully if properly funded and managed at the federal level.
Challenges associated with this recommendation are most notably the challenges to
systemic programmatic success based on changes to the current political administration or public
perceptions. As evidenced in bathroom bill debates (Murib, 2019) and in the revocation of
transgender military service approvals (Seibert et al., 2020), transgender inclusion and support is
likely to remain a hot topic issue in US politics and will likely remain the target of conservative
opposition for the foreseeable future. Given the cultural opposition that exists, a federally funded
program that seeks to provide virtual primary care services to transgender people would be a
highly visible, and therefore highly probable, public program to be cut should a conservative
political party gain majority within congress or be elected to the presidency.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 87

Recommendation 2: Establish a Coalition of Non-Profits and Government Agencies to
Educate Gender and Sexual Minorities on Navigating Local Healthcare Systems and
Defending Employment Rights
As stated previously, income was a statistically significant predictor for healthcare-
seeking behaviors, perceived physical health, and perceived mental health. Additionally, many
open-ended responses cited employment insecurity or financial insecurity as a primary concern
for transgender people. In addition to income, insurance coverage was a statistically significant
predictor for higher healthcare reception. Those who were not covered by insurance sought
healthcare fewer times than they received healthcare, on average, and utilized the Emergency
Room more than their primary care physician, suggesting a lack of intentionality in healthcare
behaviors. Given the findings related to insurance coverage and financial security as drivers for
healthcare behaviors and perceptions, a coalition of non-profits and government agencies
focused on championing healthcare access and defending healthcare and employments rights
may improve behaviors and perceptions for transgender people.
In practice, this coalition would manifest as a non-profit and local government
partnership coalition like the model utilized by the Public Health Foundation (PHF). PHF is a
non-profit that has numerous programs aimed at partnering with local governments and academic
institutions to improve access to healthcare, healthcare academic stewardship, improving
community health, and training for public health professionals and partners (Public Health
Foundation, 2023). By connecting government programs, academic and medical institutions, and
other nonprofits, this model would pave the way for improved access to healthcare for the
transgender community. Additionally, leveraging the subject matter expertise from these various
organizations would streamline the ability of this coalition to produce advocacy resources,
TRANSGENDER PERCEPTIONS OF HEALTHCARE 88

informational materials on navigating healthcare and insurance, and connecting unlawfully
terminated transgender employees with legal resources.
Challenges associated with this recommendation are rooted in the varying cultural,
political, and legal landscape from state to state. A nonprofit coalition focused on improving
access to healthcare and strengthening employment rights will be much easier to institute in
culturally liberal states, whereas the concept will likely face strong opposition in culturally
conservative states (Bonchicchio et al., 2023). Moreover, grant funding at the state and local
level will likely be highly contentious and may threaten the ability of the coalition to thrive in the
long run.
Recommendation 3: Incentivizing Human Rights Campaign Approach to Assessing and
Rewarding Gender-Affirming Communities
Community connectedness and concealment were, perhaps somewhat surprisingly,
positively correlated. Additionally, open-ended responses indicated lack or presence of
community connectedness as an influencer in health behaviors and concealment as an enabler to
receiving healthcare. One potential explanation of the unexpected relationship between
Community Connectedness and Concealment may be explained by transgender social
interactions occurring primarily in online environments. In this scenario, individual concealment
behaviors may be high due to localized, conservative cultures and laws, while sense of
community connectedness is also high due to strong online communities and friend networks
(Barr et al., 2016). Considering trends identified wherein transgender people conceal their gender
identity to secure healthcare and to achieve psychological and physical safety, efforts should be
made reduce individual feelings that gender identity concealment is required for safety. While
this can be achieved in many ways, one straightforward approach is to encourage communities to
TRANSGENDER PERCEPTIONS OF HEALTHCARE 89

foster inclusive and gender-affirming behaviors. This can be achieved by tying federal and state
incentives to municipalities scoring higher on the Human Right’s Campaign Municipality
Equality Index, which would assess community inclusiveness and rewarding those communities
that seek to make safe spaces for gender minorities (Municipality Equality Index, 2022).
In practice, this program would partner with the Human Right’s Campaign to provide
additional funding to expand the Municipality Equality Index (MEI) beyond its current scope,
which broadly focuses on state capitals and larger cities (Municipality Equality Index, 2022).
Using an “opt-in” model, cities and counties would be elect to be assessed against the HRC MEI
criteria and receive a score from 0 to 100, with higher scores indicating more inclusive
communities. Each year, when the presidential budget is allocated and approved, the collective
MEI score of the state would be used as a weighting criterion to determine allocation of federal
funding for discretionary and modernization programs. Programs that have been historically
supported by recurring, annual, federal funds would continue to be funded, but new funds
aligned to modernization efforts (e.g., Building a Better America funding) would be partially
weighted based on MEI scores (White House, 2023). Other factors such as state size, need for
proposed funding, etc. would also be a weighting factor, but the goal of this program would be to
incentivize tangible steps by local communities to demonstrate a commitment to LGBTQIA+
inclusiveness.
Challenges associated with this recommendation, much like previous recommendations,
would be the varying degree of social and pollical support based on the degree to which the
community is culturally conservative. Furthermore, negative externalities may result from
communities that wish to become more inclusive but can’t change fast enough. For example, if a
community or state decided to take measurable steps to improve inclusiveness but needed a
TRANSGENDER PERCEPTIONS OF HEALTHCARE 90

multi-year period to address restrictive laws or public policies, that state would be at a multi-year
disadvantage to receiving much-needed federal funds despite attempting to promote inclusive
culture.
Recommendation 4: Petitioning the ACGME to Incorporate Physician Training and
Pledges into Continuing Medical Education Requirements
Findings indicate that the presence or lack of physician supports influence health-seeking
and receiving behaviors for transgender patients. Whether intentionally or unintentionally, lack
of physician training may contribute to healthcare avoidance, gender identity concealment,
perceptions of discrimination and gender non-affirmation, or internalized transphobia due to
negative experiences in a healthcare setting. While there are no studies directly relating lack of
physician training in gender-affirming care procedures to gender minority stress and resilience
explanatory variables, lack of physician training is relatively well documented (Rider et al.,
2019).
In practice, this recommendation would consist of petitioning the American College of
Graduate Medical Education to: (1) adopt and acknowledge WPATH standards of care for
gender and sexual minorities (WPATH, 2012); (2) identify training requirements and outline
gender affirming curriculum required of all residency programs in the US; and (3) provide virtual
and in-person courses on gender-affirming care and require annual training credits as part of
physician continuing medical education (CME) requirements. Mandating training and adoption
of the WPATH standards of care will have profound impacts at the individual physician to
patient interaction, as the WPATH standards of care outline gender-affirming procedures in both
the treatment of medical conditions, how to interact with transgender patients, and how to
professionally assist during social transition, etc. Adopting the WPATH standards of care in U.S.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 91

medicine will dramatically improve patient interactions and minimize gender minority stress in
the healthcare setting. Developing specialized curriculum informed by transgender people for
physicians increases physician comfort and improves their ability to care for transgender patients
in the future (Kreines et al., 2022). In addition to adopting the WPATH standards of care, the
ACGME should build and deploy physician curriculum that teaches providers to ask direct
questions to better inform transgender care, improve comfort and confidence when providing
care to transgender patients, and promotes the use inclusive language in the healthcare setting
(Kreines et al., 2022). This recommendation would be supported by compiling evidence-based
research through a systematic literature review to identify evidence-based physician training and
identify the impact to transgender experiences and outcomes as measured by a longitudinal
survey of patients.
Challenges of this recommendation are that improving physician education and training
may only improve the transgender patient experience and may not mitigate against gender
minority stressors that occur outside of a healthcare setting (e.g., discrimination faced in one’s
local community). Furthermore, despite national standards for physician education across the
many medical specialties (e.g., Endocrinology, Emergency Medicine, Dermatology, etc.) training
setup and requirements may vary demonstrably from program to program and from state to state.
Recommendation 5: Address Pervasive Gender Concealment Behaviors within the
Transgender Community by Promoting Community Connectedness
As indicated in the research findings, gender identity concealment is persistent source of
pain for transgender respondents. Additionally, community connectedness was shown in
qualitative response data to mitigate gender minority stressors such as discrimination or gender
identity concealment. This recommendation seeks to provide tactical solutions to reduce the
TRANSGENDER PERCEPTIONS OF HEALTHCARE 92

perceived need to reveal gender identity as indicated in open-ended survey data by improving
transgender community connectedness. Transgender community connectedness is well
researched to improve mental health by mitigating against gender minority stressors that
transgender people often experience (Pflum et al., 2015). Community connectedness provides
healthy coping mechanisms such as humor, while simultaneously creating an environment that
supports the validation and normalization of traumatic or difficult experiences for transgender
people (Pflum et al., 2015). Group interventions for transgender youth and adults have been
proven to strengthen a sense of community connectedness while simultaneously providing
supports for transgender participants (Matsuno and Israel, 2018). Matsuno and Israel (2018)
specifically call out group therapy sessions and mentorship programs as solutions that can
increase the sense of transgender community connectedness as a group intervention to mitigate
against gender minority stress. Implementing group therapy sessions and establishing mentorship
programs would improve community connectedness and slowly diminish transgender perceived
need to conceal gender identity.
In practice, this recommendation occurs at the community and individual level by
establishing LGBTQIA+ community chapters that are focused on group therapy and peer
mentorship. Conducting a study of successful group therapy frameworks (such as group therapy
or Alcoholics Anonymous) and codifying the key lessons learned, a guidebook on starting a local
LGBTQIA+ support chapter would be developed. From there, motivated LGBTQIA+
community leaders would have the framework required to establish a local chapter, organize and
resource group therapy sessions, establish peer mentorship programs, and meet regularly with
LGBTQIA+ members of the community. Utilizing publicly available resources (e.g., city parks,
town halls, etc.) to meet and coordinating pro-bono group therapy services with licensed
TRANSGENDER PERCEPTIONS OF HEALTHCARE 93

therapists will result in minimal operating costs to each local chapter, while still achieving the
outcomes and benefits of therapy and mentorship as outlined by Matsuno and Israel (2018).
Frequent bi-weekly or monthly meetings in a group therapy setting that generally follows a
universally understood format (to be agreed upon and set forth by the attendees and the chapter
leader) will provide an opportunity for increased interaction amongst transgender people and
social supports that are critical to mitigating gender minority stress.
Challenges of this recommendation are primarily born from conservative political values
that may result in chapters being made to feel that it is not safe to meet. Additionally, because
this is a community-based and individually led recommendation there are likely to be increased
resourcing and funding challenges. Finally, participation may also present a unique challenge in
smaller communities, if the LGBTQIA+ population is small, parents of LGBTQIA+ youth will
not support participation, or if LGBQIA+ adults fear for their safety when participating, chapter
meetings may not be well attended which could have negative impacts on building community
connectedness.
Limitations and Delimitations
Limitations of this study primarily center on respondent information and responses. False
or inaccurate responses to the screener survey may have resulted in cisgender response data
being incorporated into data collection and analysis. Additionally, inaccurate responses or false
responses to select survey questions for various reasons such as medical mistrust or patient
embarrassment may influence the survey findings. Finally, the study is limited by overall survey
sample size and makeup because random sampling is impossible for survey administration
amongst transgender patients. The use of an online survey for data collection is predicated on
internet access, time availability, convenience of participants, and able-ness (e.g., computer
TRANSGENDER PERCEPTIONS OF HEALTHCARE 94

surveys may be difficult for participants with vision difficulties), which poses inherent
limitations to the study. These limitations manifested in a predominantly young, white
transwoman sample that was not truly representative of the transgender community.
Finally, a delimitation of the study is that it is purely quantitative in nature which
provides no qualitative for transgender participants to explain their healthcare experiences and
behavioral intentions in their own words. While open ended survey responses provided strong
insights and answered research questions, additional interview responses may have yielded a
stronger connection between stressors, resilience factors, and healthcare behaviors and
perceptions.
Recommendations for Future Research
As indicated in Chapter 4, the model fit for healthcare seeking, healthcare reception, and
perceived physical health was lower than expected as it relates to the gender minority stress and
resilience explanatory variables. Income and insurance coverage were the strongest predictive
factors, therefore the primary recommendation for future research would be to identify additional
explanatory variables that may improve the overall model fit. Location within the United States,
level of education, physician qualifications, physician comfortability in the treatment of
transgender patients, and many other variables may result in better fit and stronger predictive
power of the model.
The secondary recommendation from future research is born of limitations identified
during the study. Social media was the primary recruitment technique from the study which
resulted in a predominantly youthful, transwoman, white sample. The limited diversity within the
sample may not only have skewed the model fit and assessment of predictive power, but it also
means that the results of the study are only generalized to cohorts with similar demographics as
TRANSGENDER PERCEPTIONS OF HEALTHCARE 95

were found in this study. Future research should seek to diversify respondents by conducting
virtual and in-person surveys, extending the period of data collection and diversifying participant
recruitment techniques, and establishing thresholds for sample representation before moving
forward with data analysis.
The third and final recommendation for future research is to conduct true mixed-methods
studies with semi-structured or unstructured interviews that seek to identify key drivers and root
causes behind healthcare avoidance and the role that gender minority stressors play in the
healthcare setting. While the GMSR questionnaire offers insight into an individual’s exposure to
proximal and distal stressors and resilience factors, interview responses serve to tie stress
exposure to healthcare behaviors and perceptions more strongly than quantitative data alone.
Conclusion
This study sought to address the problem of healthcare avoidance by identifying what
health seeking behaviors look like and identifying which, if any, gender minority stress and
resilience factors might predict health behaviors and perceptions. Responses were collected for
213 respondents. Findings indicated various modalities and health seeking behaviors across race,
gender, income, and insurance coverage. Additionally, the importance of physician supports and
community connectedness in shaping healthcare behaviors was strongly indicated through both
quantitative survey items and open-ended responses. Additionally, data analysis identified that
income, insurance discrimination, internalized transphobia, age, and gender non-affirmation have
some predictive power when it comes to gender minority health behaviors and perceptions.
Furthermore, the theme of gender identity concealment as a method to secure healthcare and
achieve psychological and physical safety was prominent in open-ended responses. While the
TRANSGENDER PERCEPTIONS OF HEALTHCARE 96

findings of this study are specific to the conceptual model developed on the Gender Minority
Stress and Resilience Model, they tell a larger story.
The findings of this dissertation pave the way for targeted public policy recommendations
that aim to minimize gender minority stressors, improve gender minority resilience factors, and
account for general barriers to care for gender minorities. Moreover, this study suggests that,
with continued and refined research, a strong conceptual model that accurately paints the picture
of what drives gender minority healthcare behaviors and perceptions can be developed. If the US
healthcare system is able to improve upon this model of gender minority health behaviors and
perceptions, truly powerful public policies, non-profit programs, community and school-based
interventions, and specialized interventions in the healthcare setting can be developed to begin
addressing the gap of gender minority healthcare in our country.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 97

References
Abreu, R. L., Kenny, M. C., Hall, J. G., & Huff, J. (2019). Supporting transgender students:
School counselors’ preparedness, training efforts, and necessary support. Journal of LGBT
Youth, 17(1), 107–122. https://doi.org/10.1080/19361653.2019.1662755
American Academy of Family Physicians. (2019, December 12). Primary care. AAFP.
Retrieved August 5, 2022, from https://www.aafp.org/about/policies/all/primary-
care.html#:~:text=Primary%20care%20is%20the%20provision,context%20of%20family%
20and%20community.
American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders
(Third Edition ed.). Washington, DC: American Psychiatric Association.
American Psychiatric Association. (2021). Gender Dysphoria Diagnosis. Retrieved from
Psychiatry.org: https://www.psychiatry.org/psychiatrists/cultural-competency/education/t
ransgender-and-gender-nonconforming-patients/gender-dysphoria
diagnosis#:~:text=With%20the%20publication%20of%20DSM%E2%80%935%20in%2
02013%2C%20%E2%80%9Cgender,%2C%20medical%2C%20and%2
Asquith, A., Sava, L., Harris, A. B., Radix, A. E., Pardee, D. J., & Reisner, S. L. (2021). Patient-
cenered practices for engaging transgender and gender diverse patients in clinical
research studies. BMC Medical Research Methodology, 21(202), 1-15.
doi:10.1186/s12874-021-01328-4
TRANSGENDER PERCEPTIONS OF HEALTHCARE 98

Association of American Medical Colleges. (2007, March). Institutional programs and
exucational activities to address the needs of gay, lesbian, bisexual, and transgender
students and patients. Memorandum #07-07. Washington, D.C.: AAMC.
Baldwin, A., Dodge, B., Schick, V. R., Light, B., Schnarrs, P. W., Herbenick, D., & Fortenberry,
J. (2018). Transgender and genderqueer individual's experiences with health care
providers: What's working, what's not, and where do we go from here? Journal of Health
Care for the Poor and Underserved, 29(4), 1300-1318.
doi:https://doi.org/10.1353/hpu.2018.0097
Banerjee, S. C., Walters, C. B., Staley, J. M., Alexander, K., & Parker, P. A. (2018). Knowledge,
beliefs, and communication behavior of oncology health-care providers (HCPs) regarding
lesbian, gay, bisexual and transgender (LGBT) patient health care. Journal of Health
Communication, 23(4), 329-339. doi:10.1080/10810730.2018.1443527
Barr, S. M., Budge, S. L., & Adelson, J. L. (2016). Transgender community belongingness as a
mediator between strength of transgender identity and well-being. Journal of Counseling
Psychology, 63(1), 87–97. https://doi.org/10.1037/cou0000127
Bauer, G. R., Zong, X., Schein, A. I., Hammond, R., & Thind, A. (2015). Factors impacting
transgender patients’ discomfort with their family physicians: A respondent-driven
sampling survey. PLOS ONE, 1-16. https://journals.plos.org/plosone/article/file?id=
10.1371/journal.pone.0145046&type=printable
Benjamin, H. (1966). The transsexual pheonomenon. New York: The Julian Press, Inc.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 99

Blotner, C., & Rajunov, M. (2018). Engaging transgender patients: Using social media to inform
medical practice and research in transgender health. Transgender Health, 3(1), 225-228.
doi:10.1089/trgh.2017.0039
Bochicchio, L., Carmichael, A. J., Veldhuis, C., & Stefancic, A. (2023). What we lose when we
“don’t say gay”: Generational shifts in sexual identity and gender. Social Work.
https://doi.org/10.1093/sw/swad006
Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and
design. Harvard University Press.
Burke, S. E., Dvidio, J. F., Przedworski, J. M., Hardeman, R. R., Perry, S. P., Phelan, S. M., . . .
van Ryn, M. (2015). Do contact and empathy mitigate bias against gay and lesbian
people among heterosexual first-year medical students? A report from the medical
student CHANGE study. Academic Medicine, 90(5), 645-651.
doi:10.1097/ACM.0000000000000661
Centers for Disease Control and Prevention. (2019). Terminology. Centers for Disease Control
and Prevention. Retrieved August 2, 2022, from
https://www.cdc.gov/healthyyouth/terminology/sexual-and-gender-identity-terms.htm
Centers for Disease Control and Prevention. (2020). Health disparities. Centers for Disease
Control and Prevention. Retrieved August 2, 2022, from
https://www.cdc.gov/healthyyouth/disparities/index.htm
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed
methods approaches (Fifth ed.). Los Angeles: SAGE.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 100

D'Avanzo, P. A., Bass, S. B., Brajuha, J., Gutierrez-Mock, L., Ventriglia, N., Wellington, C., &
Sevelius, J. (2019). Medical mistrust and PrEP perceptions among transgender women: A
cluster analysis. Behavioral Medicine, 45(2), 143-152.
doi:10.1080/08964289.2019.1585325
Department of Health and Human Services. (2014). NCD 140.3, Transexual Surgery Docket No.
A-13-87, Decision No. 2576. Departmental Appeals Board Appellate Division.
Dietert, M., & Dentice, D. (2022). Transgender military experiences: From Obama to Trump.
Journal of Homosexuality, 1-18. doi:https://doi.org/10.1080/00918369.2021.2012866
Docter, R. F. (2008). Becoming a woman: A biography of Chrstine Jorgensen. New York: The
Haworth Press, Taylor & Francis Group .
Drabble, L. A., Veldhuis, C. B., Wootton, A., Riggle, E. D., & Hughes, T. L. (2019). Mapping
the landscape of support and safety among sexual minority women and gender non-
conforming idnviduals: Perceptions after the 2016 US presidential election. Sexuality
Research and Social Policy (16), 488-500.doi:https://doi.org/10.1007/s13178-018-0349-6
Firek, A. F., & Sawan-Garcia, R. (2018). Preparing clients for hormone therapy. In M. R. Kauth,
& J. C. Shipherd (Eds.), Adult transgender care: An interdisciplinary approach for
training mental health providers (1st ed., pp. 78-100). New York: Routledge.
Flores, A. R., Brown, T. N. T., & Herman, J. L. (2016, October). Race and ethnicity of adults
who identify as transgender in the United ... The Williams Institute. Retrieved April 17,
2023, from https://williamsinstitute.law.ucla.edu/wp-content/uploads/Race-Ethnicity-
Trans-Adults-US-Oct-2016.pdf
TRANSGENDER PERCEPTIONS OF HEALTHCARE 101

Gill-Peterson, J. (2018). Histories of the Transgender Child. Minneapolis, MN: University of
Minnesota Press. Retrieved from https://www.jstor.org/stable/10.5749/j.ctv75d87g.1
Gonzales, G., & McKay, T. (2017). What an emerging Trump administration means for lesbian,
gay, bisexual, and ransende health. Health Equity, 1(1), 83-86.
doi:10.1089/heq.2017.0002
Hampton, M.C., & Pachankis, J.E. (2018). Evidence-based approaches for improving sexual and
gender minority health by reducing minority stress. In Smalley, B.K, Warren, J.C., &
Barefoot, K.N. (Eds.) LGBT health: Meeting the needs of sexual and gender minorities
(1
st
ed., pp. 485-505). Springer Publishing Company.
Haviland, K.S., Swette, S., Kelechi, T., & Mueller, M. (2020). Barriers and facilitators to cancer
screening among LGBTQ individuals with cancer. Oncology Nursing Society, 47(1), 44-
55. https://doi.org/10.1188/20.0NF.44-55
Hendricks, M. L., & Testa, R. J. (2012). A conceptual framework for clinical work with
transgender and gender nonconforming clients: An adaptation of the minority stress
model. Professional Psychology: Research and Practice, 43(5), 460-467.
doi:10.1037/a0029597
Herman, J. L., O'Neill, K. K., & Flores, A. R. (2022, June). How many adults and youth identify
as transgender in the United States? UCLA School of Law: Williams Institute. Retrieved
from https://williamsinstitute.law.ucla.edu/publications/trans-adults-united-states/
TRANSGENDER PERCEPTIONS OF HEALTHCARE 102

Hopwood, R. A. (2018). Supporting the transgender individual in deciding their pathway. In M.
R. Kauth, & J. C. Shipherd (Eds.), Adult transgender care: An interdisciplinary approach
for training mental health professions (1st ed., pp. 61-77). New York: Routledge.
Hughto, J. M., Meyers, D. J., Mimiaga, M. J., Reisner, S. L., & Cahill, S. (2021). Uncerainty and
confusion regarding non-dsicrimination policies: Implications for the mental health of
transgender Americans. Sexuality Research and Social Policy, 1-11.
doi:https://doi.org/10.1007/s13178-021-00602-w
Jaiswal, J. (2019). Whose responsibility is it to dismantle medical mistrust? Future directions for
researchers and health care providers. Behavioral Medicine, 45(2), 188-196.
doi:10.1080/08964289.2019.1630357
Kosenko, K., Rintamaki, L., Raney, S., & Maness, K. (2013). Transgender patient perceptions of
stigma in health care contexts. Medical Care, 51(9), 819-822. Retrieved from
https://www.jstor.org/stable/42568763
Kreines, F. M., Quinn, G. P., Cardamone, S., Pi, G. E., Cook, T., Salas-Humara, C., Fino, E., &
Shaw, J. (2022). Training clinicians in culturally relevant care: A curriculum to improve
knowledge and comfort with the transgender and gender diverse population. Journal of
Assisted Reproduction and Genetics, 39(12), 2755–2766. https://doi.org/10.1007/s10815-
022-02655-1
Lefevor, G., Boyd-Rogers, C. C., Sprague, B. M., & Janis, R. A. (2019). Health disparities
between genderqueer, transgender, and cisgender individuals: An extension of minority
TRANSGENDER PERCEPTIONS OF HEALTHCARE 103

stress theory. American Psychological Association, 66(4), 385-395.
doi:10.1037/cou0000339
Matouk, K.M., & Wald, M. (2021, June 23). Gender-affirming care saves lives. Columbia
University. https://www.columbiapsychiatry.org/news/gender-affirming-care-saves-lives
Matsuno, E., & Israel, T. (2018). Psychological interventions promoting resilience among
transgender individuals: Transgender resilience intervention model (TRIM). The
Counseling Psychologist, 46(5), 632–655. https://doi.org/10.1177/0011000018787261
McPhail, D., Rountree-James, M., & Whetter, I. (2016). Addressing gaps in physician
knowledge regarding transgender health and healthcare through medical education.
Canadian Medical Education Journal, 7(2), 70-78. Retrieved from http://www.cmej.ca/
Meade, M. A., Mahmoudi, E., & Lee, S.-Y. (2014). The intersection of disability and Healthcare
Disparities: A conceptual framework. Disability and Rehabilitation, 37(7), 632–641.
https://doi.org/10.3109/09638288.2014.938176
Meyer, I. H. (2003). Prejudice, social stress, and menal health in lesbian, gay, and bisexual
populations: Conceptual issues and research evidence. Psychological Bulletin, 129(5),
674-697. doi:10.1037/0033-2909.129.5.674
Mezey, S. G. (2020). Transgeder rights: From Obama to Trump. New York: Routledge.
Michelson, M. R., & Harison, B. F. (2020). Transforming prejudice: Identity, fear, and
transgender rights. Oxford Scholarship Online.
doi:10.1093/oso/9780190068882.001.0001
TRANSGENDER PERCEPTIONS OF HEALTHCARE 104

Miner, M. H., Bockting, W. O., Romine, R. S., & Raman, S. (2012). Conducting internet
research with the transgender population: Reaching broad samples and collecting vital
data. Soc Sci Comput Rev, 30(2), 202-211. doi:10.1177/0894439311404795
Money, J. W. (1969). Sex reassignment as related to hermaphroditism and transsexualism.
Baltimore: Johns Hopkins Press.
Movement Advancement Project. (2020). LGBTQ policy spotlight: Mapping LGBTQ equality:
2010 to 2020. https://www.lgbtmap.org/file/2020-tally-report.pdf
Municipal Equality Index 2022. Human Rights Campaign. (2022). Retrieved March 5, 2023,
from https://www.hrc.org/resources/municipal-equality-index
Murib, Z. (2019). Administering biology: How “bathroom bills” criminalize and stigmatize trans
and gender nonconforming people in public space. Administrative Theory & Praxis,
42(2), 153–171. https://doi.org/10.1080/10841806.2019.1659048
Najor, A. J., Kling, J. M., Imhof, R. L., Sussman, J. D., Nippoldt, T. B., & Davidge-Pis, C. J.
(2020). Transgender health care curriculum development: A dual-site medical school
campus pilot. Health Equity, 4(1), 102-113. doi:10.1089/heq.2019.0106
Noonan, E. J., Sawning, S., Combs, R., Weingartner, L. A., Martin, L. J., Jones, V., &
Holthouser, A. (2018). Engaging the transgender community to improve medical
education and prioritize healthcare initiatives. Teaching and Learning in Medicine, 30(2),
119-132. doi:10.1080/10401334.2017.1365718
Pew Research Center. (2019, May 14). Attitudes on same-sex marriage: Public opinion on same-
sex marriage. https://www.pewforum.org/fact-sheet/changing-attitudes-on-gay-marriage/
TRANSGENDER PERCEPTIONS OF HEALTHCARE 105

Pflum S. R., Testa R. J., Balsam K. F., Goldblum P. B., Bongar B. (2015). Social support,
trans community connectedness, and mental health symptoms among transgender and
gender nonconforming adults. Psychology of Sexual Orientation and Gender Diversity, 2,
281–286.
Prabawanti, C., Dijkstra, A., Riono, P., & Hartana, G. (2015). A survey on HIV-related health-
seeking behaviors among transgender individuals in Jakarta, based on the theory of
planned behavior. BMC Public Health(15), 1-10. doi:10.1186/s12889-015-2480-0
Puckett, J.A., Cleary, P., Rossman, K., Mustanski, B., & Newcomb, M.E. (2018). Barriers to
gender-affirming care for transgender and gender nonconforming individuals. Sexuality
Research and Social Policy, 15, 48-59. https://doi.org/10.1007/s13178-017-0295-8
Rahman, M., Li, D. H., Moskowitz, D. A. (2019). Comparing the healthcare utilization and
engagement in a sample of transgender and cisgender bisexual+ persons. Archive of
Sexual Behavior, 48, 255-260. https://doi.org/10.1007/s10508-018-1164-0
Reisner, S.L., White Hughto, J.M., Dunham, E.E., Heflin, K.J., Begeny, J.B.G., Coffey-Esquivel,
J., & Cahill, S. (2015). Legal protections in public accommodations settings: A critical
public health issue for transgender and gender-nonconforming people. The Milbank
Quarterly, 93(3), 484-515. https://doi.org/10.1111/1468-0009.12127
Rider, G. N., McMorris, B. J., Gower, A. L., Coleman, E., Brown, C., & Eisenberg, M. E.
(2019). Perspectives from nurses and physicians on training needs and comfort working
with transgender and gender-diverse youth. Journal of Pediatric Health Care, 33(4), 379–
385. https://doi.org/10.1016/j.pedhc.2018.11.003
TRANSGENDER PERCEPTIONS OF HEALTHCARE 106

Schechter, L. S., & Schechter, R. B. (2018). Pursuing gender transitioning surgeries. In M. R.
Kauth, & J. C. Shipherd (Eds.), Adult transgender care: An interdisciplinary approach for
training mental health professionals (1st ed., pp. 140-160). New York: Routledge.
Schreiber, J. B., Nora, A., Stage, F., Barlow, E. A., & King, J. (2006, July/August). Reporting
structural equation modeling and confirmatory factor analysis results: A review. The
Journal of Educational Research, 99(6), 323-337.
doi:http://dx.doi.org/10.3200/JOER.99.6.323-338
Schuller, K.A. & Crawford, R.P. (2020). Impact of interpersonal client-provider relationship on
satisfaction with mental healthcare among LGBTQ+ population. Journal of Mental
Health, 1-8. https://doi.org/10.1080/09638237.2020.1793126
Seibert, D. C., Keller, N., Zapor, L., & Archer, H. (2020). Military transgender care. Journal of
the American Association of Nurse Practitioners, 32(11), 764–770.
https://doi.org/10.1097/jxx.0000000000000519
Sevelius, J. M. (2012). Gender affirmation: A framework for conceptualizing risk behavior
among transgender women of color. Sex Roles, 68(11-12), 675–689.
https://doi.org/10.1007/s11199-012-0216-5
Sevelius, J. M., Kealey, J., Calma, N., & Arnold, E. (2016). 'I am not a man': Trans-specific
barriers and facilitators to PrEP acceptability among transgender women. Global Public
Health, 11(7-8), 1060-1075. doi:10.1080/17441692.2016.1154085
Sexual & Gender Minority Research Office (SGMRO). (2017). Sexual & Gender Minority
Research Portfolio Analysis. U.S. Department of Health and Human Services, National
TRANSGENDER PERCEPTIONS OF HEALTHCARE 107

Institutes of Health. https://dpcpsi.nih.gov/sites/default/files/SGMR_PortfolioAnalysis
2017_ PubLayout_FV-RF508_02.pdf
She, R., Mo, P. K., Cai, Y., Ma, T., Liu, Y., & Lau, J. T. (2021). Mental health service utilisation
among transgender women sex workers who are at risk of mental health problems in
Shenyang, China: An application of minority stress theory. Health and Social Care in the
Community, 1-13. doi:10.1111/hsc.13501
Slagstad, K. (2021). The political nature of sex - Transgender in the history of medicine. (D.
Malina, Ed.) The New England Journal of Medicine, 384(11), 1070-1074.
doi:10.1056/NEJMms2029814
Sloan, C. A., & Berke, D. S. (2018). Dialectical behavior therapy as a treatment option for
complex cases of gender dysphoria. In M. R. Kauth, & J. C. Shipherd (Eds.), Adul
transgender care: An interdisciplinary approach for training mental health professionals
(1st ed., pp. 123-139). New York: Routledge.
Soper, D. (2022, June 23). Multiple Regression Sample Size Calculator. Retrieved from
Analytics Calculators: https://www.analyticscalculators.com/calculator.aspx?id=1
Strousma, D. (2014). The state of transgender health care: Policy, law, and medical frameworks.
American Journal of Public Health, 104, 31-38. doi:10.2105/AJPH.2013.301789
Substance Abuse and Mental Health Services Administration. (2020). 2020 State Profile-United
States and Other Jurisdictions National Survey of Substance Abuse Treatment Services.
SAMHSA. Retrieved March 4, 2023 from https://www.samhsa.gov/data/quick-statistics-
results?qs_type=nssats&state=United%20States&year=2020
TRANSGENDER PERCEPTIONS OF HEALTHCARE 108

Substance Abuse and Mental Health Services Administration. (2022). SAMHSA FY2022 Budget
in Brief. SAMHSA. Retrieved March 3, 2023, from https://www.samhsa.gov/site
s/default/files /samhsa-fy-2022-bib.pdf
Sutter, M. E. (2017, May). An integrated behavioral model of healthcare utilization among
transgender and gender-noncomforming adults. Richmond, Virginia: Virginia
Commonwealth University. Retrieved from https://scholarscompass.vcu.edu/etd/4799
Tan, K. K., Treharne, G. J., Ellis, S. J., Schidt, J. M., & Veale, J. F. (2020). Gender minority
stress: A critical review. Journal of Homosexuality, 67(10), 1471-1489.
doi:10.1080/00918369.2019.1591789
Tesch, R. (1990). Qualitative research: Analysis types and software tools. New York: Falmer.
Testa, R. J., Bliss, W., Balsam, K. F., Michaels, M. S., Rogers, M. L., & Joiner, T. (2017).
Suicidal ideation in transgender people: Gender minority stress and interpersonal theory
factors. Journal of Abnormal Psychology, 126(1), 125-136. doi:10.1037/abn0000234
Testa, R. J., Habarth, J., Peta, J., Balsam, K., & Bockting, W. (2015). Development of the gender
minority stress and resilience measure. Psyhology of Sexual Orientation and Gender
Diversity, 2(1), 65-77. doi:10.1037/sgd0000081
The Gallup Organization. (2021). LGBT Rights. Retrieved from GALLUP:
https://news.gallup.com/poll/1651/gay-lesbian-rights.aspx
The United States Government. (2023, February 21). Building a better america. The White
House. Retrieved March 14, 2023, from https://www.whitehouse.gov/build/
TRANSGENDER PERCEPTIONS OF HEALTHCARE 109

TRAIN from the Public Health Foundation. What is the Train Learning Network? (n.d.).
Retrieved March 3, 2023, from http://www.phf.org/programs/TRAIN/Pages/What
_is_the_TRAIN_Learning_Network.aspx
Tucker, R. P., Testa, R. J., Reger, M. A., Simpson, T. L., Jillian, C. S., & Lehavot, K. (2019).
Current and military-specific gender minority stress factors and their relationship with
suicide ideation in transgender veterans. Suicide and Life-Threatening Behavior, 49(1),
155-166. doi:10.1111/sltb.12432
van Eijk, M. (2017). Insuring care: Paperwork, insurance rules, and clinical labor at a U.S.
transgender clinic. Culture, Medicine, and Psychiatry, 41(4), 590-608.
doi:10.1007/s11013-017-9529-8
Veldhuis, C. B., Drabble, L., Riggle, E. D., Wootton, A. R., & Hughes, T. L. (2018). "I fear for
my safety, but want to show bravery for others": Violence and discrimination concerns
among transgender and gender non-conforming individuals after the 2016 election.
Violence and Gender, 5(1), 1-11. doi:10.1089/vio.2017.0032
Wahlert, L., & Gill, S. (2017). Pathological, disabled, transgender: The ethics, history, law, and
contradictions in moels that best serve transgender rights. Kennedy Institute of Ethics
Journal, 27(2), 249-266. doi:https://doi.org/10.1353/ken.2017.0017
Warwick, R. M., & Shumer, D. E. (2021). Gender-affirming multidsciplinary care for
transgender and non-binary children and adolescents. Children's Health Care, 1-26.
doi:https://doi.org/10.1080/02739615.2021.2004146
TRANSGENDER PERCEPTIONS OF HEALTHCARE 110

White Hughto, J.M., Murchison, G.R., Clark, K., Pachankis, J.E., Reisner, S.L. (2016)
Geographic and individual differences in healthcare access for U.S. transgender adults: A
multilevel analysis. LGBT Health, 3(6), 424-433. https://doi.org/10.1089/lgbt.2016.0044
Witt, H., & Medina-Martinez, K. (2022). Transgender rights & the urgent need for social work
advocacy. Social Work in Public Health, 37(1), 28-32.
doi:https://doi.org/10.1080/19371918.2021.1970685
World Professional Association for Transgender Health. (2012). Standards of care for the health
of transsexual, transgender, and gender nonconforming people (7th ed.). Retrieved from
https://www.wpath.org/publications/soc
Wylie, K., Knudson, G., Khan, S. I., Bonierbale, M., Watanyusakul, S., & Baral, S. (2016).
Serving transgender people: Clinical considerations and service delivery models in
transgender health. Lancet, 388, 401-411. doi:10.1016/S0140-6736(16)00682-6
Zelin, N. S., Hasings, C., Bealuieu-Jones, B. R., Scott, C., Rodriguez-Villa, A., Duarte, C.,
Adami, A. J. (2018). Sexual and gender minority health in medical curiccula in new
england: A pilot study of medical studen comfort, competence, and perception of
curicula. Medical Education Online, 23(1), 1-14. doi:10.1080/10872981.2018.1461513

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Appendix A: Social Media Recruitment Plan and Fliers

Date Activity Date Activity
November 15,
2022
Test survey QR code, update
recruitment flier with live survey
code/access link.
January 3,
2023
Post Survey Flier to social media sites*
and in person at locations (if needed)
below**. Post Interview Only Flier to
social media sites and in person at
locations, if needed.
December 1,
2022
Open survey in Qualtrics. Post
Survey Flier to social media sites*
and in person at locations
below**.
January 10,
2023
Check survey response rates, identify
number of interview participant
responses within Component #3 of
survey. Consolidate list of interview
participants indicating interest from
Interview Only flier, if needed.
December 10,
2022
Check survey response rates,
identify number of interview
participant responses within
Component #3 of Survey
January 15,
2023
Post Survey Flier to social media sites*
and in person at locations (if needed)
below**. Post Interview Only Flier to
social media sites and in person at
locations, if needed.
December 15,
2022
Post Survey Flier to social media
sites* and in person at locations (if
needed) below**.
January 30,
2023
Close survey. Remove all flyers from
physical locations.
December 20,
2022
Make determination on whether or
not to post an “Interview Only”
flyer based on the number of
survey participants willing to
conduct an interview.
● If less than 5 survey
participants indicate
willingness to conduct
survey= post “Interview Only”
flyer
● If 6 or more survey
participants indicate
willingness to conduct
interview, do not post flier
February 1,
2023
Begin data analysis and validation.
* Facebook, Instagram, Reddit, and LinkedIn sites and/or forums dedicated to transgender community members
**Mayo Clinic, University of Minnesota, and local LGBTQIA+ friendly bars, coffee shops, and restaurants in
Minnesota
TRANSGENDER PERCEPTIONS OF HEALTHCARE 112

Survey Flier

TRANSGENDER PERCEPTIONS OF HEALTHCARE 113

Appendix B: Survey
Survey #1, Component #1 (Screener)
1. What is your age?
____Years

2. Do you consider yourself cisgender?
*Cisgender means your personal gender identity matches the sex you were assigned at birth
Yes
No

3. Do you reside in the United States of America?
Yes
No

*Participants who enter an age of 18 years or greater and answer no to question 2 and yes to
question 3, will be admitted to the second component of the survey.

Survey #1, Component #2 (Data Collection)
Informed Consent
Study Information:
Title: Examining Transgender Healthcare Utilization: A Quantitative Study of Gender
Minority Stress and Resilience Factors in Predicting Healthcare Behavioral Intentions
Principal Investigator: Sebastian Smoak
Faculty Advisor: Nicole MacCalla, PhD
IRB Approving Institution: University of Southern California

Voluntary Participation
Taking part in this research project is voluntary. You do not have to participate and you can
stop at any time. Please take time to read this entire form and ask questions before deciding
whether to take part in this research project.

Confidentiality
Results of this study will be entirely confidential and will be non-attributional (meaning no
responses can be traced back to a single individual). Name and addresses will be collected at
the end of the survey for the purposes of mailing or emailing a $5 gift card, but all personal
identifying information will be stored separately from survey results, using cloud-based multi
factor authentication security.

Importance and Purpose of Study
This purpose of this study is to collect survey data that will aid in examining which factors
most influence the transgender community’s primary healthcare utilization behaviors and
expectations. This study is important because it will be used to generate insights and
recommendations for the healthcare system, which may improve transgender patient primary
care utilization and experience.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 114

Who can participate in this study?
Participants in this study should be based in the United States, over the age of 18, and should
identify as transgender (defined as identifying as a gender other than the gender assigend at
birth).

What is being studied and what will happen during the study?
The study will ask questions regarding transgender patient experiences such as instances of
non-affirmation of gender identity, rejection, victimization, gender identity concealment,
negative expectations in healthcare settings, and how patients feel about their own trans-ness.
Additionally, the survey will ask questions about transgender patient attitudes, expectations,
beliefs, and intentions about utilizing primary health care services. If at any time, these
questions make you feel uncomfortable, please feel free to skip the question or exit the survey
at any time.

How long is this survey?
This survey will take approximately 20 minutes to complete.

What is done with survey results?
Survey results will be stored in a secure, cloud-based platform. Data from the survey will be
used to analyze which factors best predict a study participant’s likelihood to utilize primary
care services. Only the principal investigator and a statistical consultant will have access to
survey data. Survey data will not be made available to other researchers or used for future
research.

Who should I contact with additional questions?
For any questions related to the study, please contact smoak@usc.edu.

Will I receive compensation?
There will be no compensation.

Consent
By checking the box below, you are agreeing to participate in this study. If you have any
questions about the study after you sign this document, you can contact the study team using
the information in Section 9 provided above.
● I consent to participate in this study.
Demographic Questions
What is your current gender identity? (Check all that apply)
_ Trans male/Trans man
_ Trans female/Trans woman
_ Genderqueer/Gender-nonconforming
_ Different identity (please state): _________

Have you had health insurance for the past 12 months?
TRANSGENDER PERCEPTIONS OF HEALTHCARE 115

Question about type of insurance

Question about how they rate their health

What is your race/ethnicity: Other; African American or Black; Asian or Asian American;
American Indian, Alaska Native, or First Nation; Native Hawaiian or other Pacific Islander;
European American or White; Hispanic or Latino; Declined to respond

What is your annual household income: $0-$9,999; $10,000-$19,999; $20,000-$29,999;
$30,000-$39,999; $40,000-$49,999; $50,000-$59,999; $60,000-$69,999; $70,000-$79,999;
$80,000-$89,999; $90,000-$99,999; $100,000-$149,999; Over $150,000; Declined to Respond

How many people are in your household?

In which state do you reside [Drop Down List of All States]
Section A: Perceived Physical and Mental Health
(Max Score: 10)
1. On a scale of 1 to 10 (10 being the most healthy), how would you rate your own physical
health?
2. On a scale of 1 to 10 (10 being the most healthy), how would you rate your own mental
health?
Section B: Healthcare Utilization
(No max score, continuous)
Note: Primary care is considered routine healthcare services for medical checkups,
preventative care, common illnesses, or seeking referrals to specialist care. Primary care is
NOT specialist care such as dermatology, surgery, or endocrinology.

1. In the past 12 months, how many times did you seek out primary healthcare services for
your own healthcare needs, even if you were ultimately unsuccessful in getting healthcare
(e.g., couldn’t get an appointment in time)?
Do NOT include visits to mental health professionals and specialist visits.

_______________visits

2. In the past 12 months, how many times did you receive primary healthcare services for
your own healthcare needs (e.g., successfully scheduled and attended an appointment)?
Do NOT include visits to mental health professionals and specialist visits.

_______________visits
*If response is 1 or more visits, then

2a.) Adjust the slider to indicate roughly what percentage of your visits were the following:
___ Related to Monkeypox or COVID-19
___ Received care virtually over Zoom or video software
TRANSGENDER PERCEPTIONS OF HEALTHCARE 116

___ Received care telephonically
___ Received care in person
___ Received care through a primary care scheduled appointment at your doctor’s office
___ Received care through the emergency department or emergency room of a hospital
___ Received care through a local urgent care clinic
___ Received care through a schedule appointment with a local non-profit or LGBTQIA+
clinic
Section C: GSMR: Gender Related Discrimination
(0=No; 1=Yes; Range: 0-5)
Directions: Please check all that apply (for example, you may check both after age 18 and in
the past year columns if both are true). In this survey gender expression means how
masculine/feminine/androgynous one appears to the world based on many factors such as
mannerisms, dress, personality, etc. All other items. Please indicate how much you agree with
the following statements.

Response options:4-point scale of: Never; Yes, before age 18; Yes, after age 18; Yes, in the
past year
1. I have had difficulty getting medical or mental health treatment (transition-related or other)
because of my gender identity or expression.
2. Because of my gender identity or expression, I have had difficulty finding a bathroom to use
when I am out in public.
3. I have experienced difficulty getting identity documents that match my gender identity.
4. I have had difficulty finding housing or staying in housing because of my gender identity or
expression.
5. I have had difficulty finding employment or keeping employment, or have been denied
promotion because of my gender identity or expression.
Section D: GSMR: Gender Related Rejection
(0=No; 1=Yes; Range: 0-6)
Response options:4-point scale of: Never; Yes, before age 18; Yes, after age 18; Yes, in the
past year
1. I have had difficulty finding a partner or have had a relationship end because of my gender
identity or expression.
2. I have been rejected or made to feel unwelcome by a religious community because of my
gender identity or expression.
3. I have been rejected by or made to feel unwelcome in my ethnic/racial community because
of my gender identity or expression.
4. I have been rejected or distanced from friends because of my gender identity or expression.
5. I have been rejected at school or work because of my gender identity or expression.
6. I have been rejected or distanced from my family because of my gender identity or
expression.
Section E: GSMR: Gender Related Victimization
(0=Strongly Disagree; 4=Strongly Agree; Range 0-20)
Response options: 4-point scale of: Never; Yes, before age 18; Yes, after age 18; Yes, in the
past year
1. I have been verbally harassed or teased because of my gender identity or expression. (For
example, being called “it”)
TRANSGENDER PERCEPTIONS OF HEALTHCARE 117

2. I have been threatened with being outed or blackmailed because of my gender identity or
expression.
3. I have had my personal property damaged because of my gender identity or expression.
4. I have been threatened with physical harm because of my gender identity or expression.
5. I have been pushed, shoved, hit, or had something thrown at me because of my gender
identity or expression.
Section F: GSMR: Non-Affirmation of Gender Identity
(0=Strongly Disagree; 4=Strongly Agree; Range 0-24)
Response options: 5-point scale from strongly disagree to strongly agree
1. I have to repeatedly explain my gender identity to people or correct the pronouns people
use.
2. I have difficulty being perceived as my gender.
3. I have to work hard for people to see my gender accurately.
4. I have to be “hypermasculine” or “hyperfeminine” in order for people to accept my gender.
5. People don't respect my gender identity because of my appearance or body.
6. People don't understand me because they don't see my gender as I do.
Section G: GSMR: Internalized Transphobia
(0=Strongly Disagree; 4=Strongly Agree; Range 0-32)
Response options: 5-point scale from strongly disagree to strongly agree
1. I resent my gender identity or expression.
2. My gender identity or expression makes me feel like a freak.
3. When I think of my gender identity or expression, I feel depressed.
4. When I think about my gender identity or expression, I feel unhappy.
5. Because of my gender identity or expression, I feel like an outcast.
6. I often ask myself: Why can’t my gender identity or expression just be normal?
7. I feel that my gender identity or expression is embarrassing.
8. I envy people who do not have a gender identity or expression like mine.
Section H: GSMR: Negative Expectations
(0=Strongly Disagree; 4=Strongly Agree; Range 0-36)
Question to determine appropriate wording for items regarding negative expectations for the
future and nondisclosure: Do you currently live in your affirmed gender* all or almost all of
the time? (*Your affirmed gender is the one you see as accurate for yourself.)

Response options: Yes, I live in my affirmed gender most or all of the time; No, I don’t live in
my affirmed gender most or all of the time
If yes: use “history” in items below. If no: use “identity” in items below.

Negative expectations for the future
Response options: 5-point scale from strongly disagree to strongly agree
1. If I express my gender IDENTITY/HISTORY, others wouldn’t accept me.
2. If I express my gender IDENTITY/HISTORY, employers would not hire me.
3. If I express my gender IDENTITY/HISTORY, people would think I am mentally ill or
“crazy.”
4. If I express my gender IDENTITY/HISTORY, people would think I am disgusting or
sinful.
5. If I express my gender IDENTITY/HISTORY, most people would think less of me.
TRANSGENDER PERCEPTIONS OF HEALTHCARE 118

6. If I express my gender IDENTITY/HISTORY, most people would look down on me.
7. If I express my gender IDENTITY/HISTORY, I could be a victim of crime or violence.
8. If I express my gender IDENTITY/HISTORY, I could be arrested or harassed by police.
9. If I express my gender IDENTITY/HISTORY, I could be denied good medical care.
Section I: GSMR: Concealment
(0=Strongly Disagree; 4=Strongly Agree; Range 0-20)
Response options: 5-point scale from strongly disagree to strongly agree
1. Because I don't want others to know my gender history, I don’t talk about certain
experiences from my past or change parts of what I will tell people.
2. Because I don't want others to know my gender history, I modify my way of speaking.
3. Because I don't want others to know my gender history, I pay special attention to the way I
dress or groom myself.
4. Because I don't want others to know my gender history, I avoid exposing my body, such as
wearing a bathing suit or nudity in locker rooms.
5. Because I don't want others to know my gender history, I change the way I walk, gesture,
sit, or stand.
Section J: GSMR: Community Connectedness
(0=Strongly Disagree; 4=Strongly Agree; Range 0-20)
Response options: 5-point scale from strongly disagree to strongly agree:
1. I feel part of a community of people who share my gender identity.
2. I feel connected to other people who share my gender identity.
3. When interacting with members of the community that shares my gender identity, I feel like
I belong.
4. I'm not like other people who share my gender identity.
5. I feel isolated and separate from other people who share my gender identity.
Section K: GSMR: Pride
(0=Strongly Disagree; 4=Strongly Agree; Range 0-32)
Response options: 5-point scale from strongly disagree to strongly agree:
1. My gender identity or expression makes me feel special and unique.
2. It is okay for me to have people know that my gender identity is different from my sex
assigned at birth.
3. I have no problem talking about my gender identity and gender history to almost anyone.
4. It is a gift that my gender identity is different from my sex assigned at birth.
5. I am like other people but I am also special because my gender identity is different from my
sex assigned at birth.
6. I am proud to be a person whose gender identity is different from my sex assigned at birth.
7. I am comfortable revealing to others that my gender identity is different from my sex
assigned at birth.
8. I'd rather have people know everything and accept me with my gender identity and gender
history.
Section L: Open Ended Response
Please use this space to share any experiences that informed your responses above or if you
would like to elaborate on any of your responses.

Survey #2 (Follow Up Interview)
1. Please enter your first and last name:
TRANSGENDER PERCEPTIONS OF HEALTHCARE 119

2. Please enter your full mailing address:

3. Please enter your email address:

4. Please indicate your willingness to participate in a follow up phone or Zoom interview
(Yes/No).

TRANSGENDER PERCEPTIONS OF HEALTHCARE 120

Appendix C: Construct Map
The construct map below demonstrates how the conceptual framework, the regression
model, and the various sections of the survey are mapped together.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 121

Appendix D: Code Book for Open-Ended Survey Items
Code Description of Code

Discrimination
Difficulty receiving healthcare, documentation, housing, or employment due to gender-based
discrimination.
Victimization
Rejection from a religious, ethnic/racial, social, work, family, or school communities due to
gender identity/expression
Rejection
Past instances of verbal/physical harassment and assault, threats or blackmail, and property
damage due to gender identity/expression
Non-
Affirmation

Experiences related to misgendering, difficulty being perceived as the appropriate gender,
and/or changing behavior to affirm gender
Concealment
Feelings of internal resentment or depression/unhappiness when thinking of one’s gender
identity
Internalized
Transphobia Feelings of uniqueness or special-ness related to being transgender or gender non-conforming
Negative
Expectations
Expectations that one might struggle with acceptance, employment, perceptions of being
‘crazy’, or fear of crime/harassment/violence
Pride
Efforts taken by respondents to avoid expressing their gender identity (e.g., changing voice or
the way one walks)
Community
Connectedness Feelings of closeness/support or isolation from one’s community, friends, or social support
Financial
Insecurity
Expressions of concern about resources, money, employment, or housing as an inhibitor to
seeking/receiving care.
Physician
Support
Expressions of appreciation or disdain for physician’s and the culture they cultivate in the
healthcare setting. Ranges from discriminatory denial of care to LGBTQIA+ affirming
providers.

TRANSGENDER PERCEPTIONS OF HEALTHCARE 122

Appendix E: Pearson-Product Moment Q-Q Plots Support RQ#3
A Pearson's product-moment correlation was run to assess the relationship between
discrimination score and gender rejection score. 213 participants were recruited, and preliminary analyses
showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in
SPSS version 28.0. There was a statistically significant, moderate positive correlation between
discrimination score and gender rejection score, r(213) = .59, p < .01, with discrimination scores
explaining 36% of the variation in gender rejection scores as shown in Figure 4 below.
Figure 4
Correlation of Discrimination and Gender Rejection

A Pearson's product-moment correlation was run to assess the relationship between
discrimination score and gender victimization score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, moderate positive correlation between
discrimination score and gender victimization score, r(213) = .52, p < .01, with discrimination scores
explaining 28% of the variation in gender victimization scores as shown in Figure 5 below.
Figure 5
Correlation of Discrimination and Gender Victimization
TRANSGENDER PERCEPTIONS OF HEALTHCARE 123

A Pearson's product-moment correlation was run to assess the relationship between
discrimination score and gender non-affirmation score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, small positive correlation between
discrimination score and gender non-affirmation score, r(213) = .21, p < .01, with discrimination scores
explaining 5% of the variation in gender non-affirmation scores as shown in Figure 6 below.
Figure 6
Correlation of Discrimination and Gender Non-Affirmation

TRANSGENDER PERCEPTIONS OF HEALTHCARE 124

A Pearson's product-moment correlation was run to assess the relationship between
discrimination score and internalized transphobia score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, small positive correlation between
discrimination score and internalized transphobia score, r(213) = .19, p < .01, with discrimination scores
explaining 3% of the variation in internalized transphobia scores as shown in Figure 7 below.
Figure 7
Correlation of Discrimination and Internalized Transphobia
TRANSGENDER PERCEPTIONS OF HEALTHCARE 125

A Pearson's product-moment correlation was run to assess the relationship between
discrimination score and negative expectations score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, moderate positive correlation between
discrimination score and negative expectations score, r(213) = .39, p < .01, with discrimination scores
explaining 16% of the variation in negative expectations scores as shown in Figure 8 below.
Figure 8
Correlation of Discrimination and Negative Expectations
TRANSGENDER PERCEPTIONS OF HEALTHCARE 126

A Pearson's product-moment correlation was run to assess the relationship between
discrimination score and concealment score. 213 participants were recruited, and preliminary analyses
showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in
SPSS version 28.0. There was a statistically significant, small positive correlation between discrimination
score and concealment score, r(213) = .23, p < .01, with discrimination scores explaining 6% of the
variation in concealment scores as shown in Figure 9 below.
Figure 9
Correlation of Discrimination and Concealment
TRANSGENDER PERCEPTIONS OF HEALTHCARE 127

A Pearson's product-moment correlation was run to assess the relationship between gender
rejection score and gender victimization score. 213 participants were recruited, and preliminary analyses
showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in
SPSS version 28.0. There was a statistically significant, moderate positive correlation between gender
rejection score and gender victimization score, r(213) = .57, p < .01, with gender rejection scores
explaining 33% of the variation in gender victimization scores as shown in Figure 10 below.
Figure 10
Correlation of Gender Rejection and Gender Victimization

TRANSGENDER PERCEPTIONS OF HEALTHCARE 128

A Pearson's product-moment correlation was run to assess the relationship between gender
rejection score and gender non-affirmation score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, small positive correlation between gender
rejection score and gender non-affirmation score, r(213) = .22, p < .01, with gender rejection scores
explaining 5% of the variation in gender non-affirmation scores as shown in Figure 11 below.
Figure 11
Correlation of Gender Rejection and Gender Non-Affirmation
TRANSGENDER PERCEPTIONS OF HEALTHCARE 129

A Pearson's product-moment correlation was run to assess the relationship between gender
rejection score and internalized transphobia score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, small positive correlation between gender
rejection score and internalized transphobia score, r(213) = .33, p < .01, with gender rejection scores
explaining 10% of the variation in internalized transphobia scores as shown in Figure 12 below.
Figure 12
Correlation of Gender Rejection and Internalized Transphobia
TRANSGENDER PERCEPTIONS OF HEALTHCARE 130

A Pearson's product-moment correlation was run to assess the relationship between gender
rejection score and negative expectations score. 213 participants were recruited, and preliminary analyses
showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in
SPSS version 28.0. There was a statistically significant, moderate positive correlation between gender
rejection score and negative expectations score, r(213) = .44, p < .01, with gender rejection scores
explaining 20% of the variation in negative expectations scores as shown in Figure 13 below.
Figure 13
Correlation of Gender Rejection and Negative Expectations
TRANSGENDER PERCEPTIONS OF HEALTHCARE 131

A Pearson's product-moment correlation was run to assess the relationship between gender
rejection score and concealment score. 213 participants were recruited, and preliminary analyses showed
the relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in SPSS
version 28.0. There was a statistically significant, small positive correlation between gender rejection
score and concealment score, r(213) = .35, p < .01, with gender rejection scores explaining 12% of the
variation in concealment scores as shown in Figure 13 below.
Figure 14
Correlation of Gender Rejection and Concealment
TRANSGENDER PERCEPTIONS OF HEALTHCARE 132

A Pearson's product-moment correlation was run to assess the relationship between gender
rejection score and pride score and gender rejection score and community connectedness score. No
statistical significance was identified for either of these two relationships. Additionally, a Pearson’s
product-moment correlation was run to assess the relationship between gender victimization and gender
non-affirmation. No statistical significance was identified for this relationship, as shown in Table 12
above.
A Pearson's product-moment correlation was run to assess the relationship between gender
victimization score and internalized transphobia score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, small positive correlation between gender
victimization score and internalized transphobia score, r(213) = .24, p < .01, with gender victimization
scores explaining 5% of the variation in internalized transphobia scores as shown in Figure 15 below.
Figure 15
Correlation of Gender Victimization and Internalized Transphobia
TRANSGENDER PERCEPTIONS OF HEALTHCARE 133

A Pearson's product-moment correlation was run to assess the relationship between gender
victimization score and negative expectations score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, small positive correlation between gender
victimization score and negative expectations score, r(213) = .36, p < .01, with gender victimization
scores explaining 13% of the variation in negative expectations scores as shown in Figure 16 below.
Figure 16
Correlation of Gender Victimization and Negative Expectations
TRANSGENDER PERCEPTIONS OF HEALTHCARE 134

A Pearson's product-moment correlation was run to assess the relationship between gender
victimization score and concealment score. 213 participants were recruited, and preliminary analyses
showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in
SPSS version 28.0. There was a statistically significant, small positive correlation between gender
victimization score and concealment score, r(213) = .28, p < .01, with gender victimization scores
explaining 8% of the variation in concealment scores as shown in Figure 17 below.
Figure 17
Correlation of Gender Victimization and Concealment
TRANSGENDER PERCEPTIONS OF HEALTHCARE 135

A Pearson's product-moment correlation was run to assess the relationship between gender
victimization score and pride score and gender victimization score and community connectedness score.
No statistical significance was identified for either of these two relationships.
A Pearson's product-moment correlation was run to assess the relationship between gender non-
affirmation score and internalized transphobia score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, moderate positive correlation between
gender non-affirmation score and internalized transphobia score, r(213) = .43, p < .01, with gender non-
affirmation scores explaining 19% of the variation in internalized transphobia scores as shown in Figure
18 below.
Figure 18
Correlation of Gender Non-Affirmation and Internalized Transphobia
TRANSGENDER PERCEPTIONS OF HEALTHCARE 136

A Pearson's product-moment correlation was run to assess the relationship between gender non-
affirmation score and negative expectations score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, moderate positive correlation between
gender non-affirmation score and negative expectations score, r(213) = .54, p < .01, with gender non-
affirmation scores explaining 30% of the variation in negative expectations scores as shown in Figure 19
below.
Figure 19
Correlation of Gender Non-Affirmation and Negative Expectations
TRANSGENDER PERCEPTIONS OF HEALTHCARE 137

A Pearson's product-moment correlation was run to assess the relationship between gender non-
affirmation score and concealment score. 213 participants were recruited, and preliminary analyses
showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in
SPSS version 28.0. There was a statistically significant, small positive correlation between gender non-
affirmation score and concealment score, r(213) = .39, p < .01, with gender non-affirmation scores
explaining 15% of the variation in concealment scores as shown in Figure 20 below.
Figure 20
Correlation of Gender Non-Affirmation and Concealment
TRANSGENDER PERCEPTIONS OF HEALTHCARE 138

A Pearson's product-moment correlation was run to assess the relationship between gender non-
affirmation score and pride score. 213 participants were recruited, and preliminary analyses showed the
relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in SPSS
version 28.0. There was a statistically significant, moderate positive correlation between gender non-
affirmation score and pride score, r(213) = .40, p < .01, with gender non-affirmation scores explaining
16% of the variation in pride scores as shown in Figure 20 below.
Figure 21
Correlation of Gender Non-Affirmation and Pride
TRANSGENDER PERCEPTIONS OF HEALTHCARE 139

A Pearson's product-moment correlation was run to assess the relationship between gender non-
affirmation score and community connectedness score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, small positive correlation between gender
non-affirmation score and community connectedness score, r(213) = .30, p < .01, with gender non-
affirmation scores explaining 9% of the variation in community connectedness scores as shown in Figure
22 below.
Figure 22
Correlation of Gender Non-Affirmation and Community Connectedness
TRANSGENDER PERCEPTIONS OF HEALTHCARE 140

A Pearson's product-moment correlation was run to assess the relationship between internalized
transphobia score and negative expectations score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, moderate positive correlation between
internalized transphobia score and negative expectations score, r(213) = .56, p < .01, with internalized
transphobia scores explaining 31% of the variation in negative expectations scores as shown in Figure 23
below.
Figure 23
Correlation of Internalized Transphobia and Negative Expectations
TRANSGENDER PERCEPTIONS OF HEALTHCARE 141

A Pearson's product-moment correlation was run to assess the relationship between internalized
transphobia score and concealment score. 213 participants were recruited, and preliminary analyses
showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in
SPSS version 28.0. There was a statistically significant, moderate positive correlation between
internalized transphobia score and concealment score, r(213) = .56, p < .01, with internalized transphobia
scores explaining 31% of the variation in concealment scores as shown in Figure 24 below.
Figure 24
Correlation of Internalized Transphobia and Concealment

TRANSGENDER PERCEPTIONS OF HEALTHCARE 142

A Pearson's product-moment correlation was run to assess the relationship between internalized
transphobia score and pride score and internalized transphobia score and community connectedness score.
No statistical significance was identified for either of these two relationships, as indicated in Table 12
above.
A Pearson's product-moment correlation was run to assess the relationship between negative
expectations score and concealment score. 213 participants were recruited, and preliminary analyses
showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in
SPSS version 28.0. There was a statistically significant, large positive correlation between negative
expectations score and concealment score, r(213) = .68, p < .01, with negative expectations scores
explaining 47% of the variation in concealment scores as shown in Figure 25 below.
Figure 25
Correlation of Negative Expectations and Concealment

TRANSGENDER PERCEPTIONS OF HEALTHCARE 143

A Pearson's product-moment correlation was run to assess the relationship between negative
expectations score and pride score. 213 participants were recruited, and preliminary analyses showed the
relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in SPSS
version 28.0. There was a statistically significant, small positive correlation between negative
expectations score and pride score, r(213) = .25, p < .01, with negative expectations scores explaining 6%
of the variation in pride scores as shown in Figure 26 below.
Figure 26
Correlation of Negative Expectations and Pride
TRANSGENDER PERCEPTIONS OF HEALTHCARE 144

A Pearson's product-moment correlation was run to assess the relationship between negative
expectations score and community connectedness score. 213 participants were recruited, and preliminary
analyses showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q
plots in SPSS version 28.0. There was a statistically significant, small positive correlation between
negative expectations score and community connectedness score, r(213) = .28, p < .01, with negative
expectations scores explaining 8% of the variation in pride scores as shown in Figure 27 below.
Figure 27
Correlation of Negative Expectations and Community Connectedness

TRANSGENDER PERCEPTIONS OF HEALTHCARE 145

A Pearson's product-moment correlation was run to assess the relationship between concealment
score and pride score. 213 participants were recruited, and preliminary analyses showed the relationship
to be linear with both variables normally distributed, as assessed by Q-Q plots in SPSS version 28.0.
There was a statistically significant, small positive correlation between concealment score and pride
score, r(213) = .15, p < .05, with concealment scores explaining 2% of the variation in pride scores as
shown in Figure 28 below.
Figure 28
Correlation of Concealment and Pride
TRANSGENDER PERCEPTIONS OF HEALTHCARE 146

A Pearson's product-moment correlation was run to assess the relationship between concealment
score and community connectedness score. 213 participants were recruited, and preliminary analyses
showed the relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in
SPSS version 28.0. There was a statistically significant, small positive correlation between concealment
score and community connectedness score, r(213) = .38, p < .01, with concealment scores explaining 14%
of the variation in community connectedness scores as shown in Figure 29 below.
Figure 29
Correlation of Concealment and Community Connectedness
TRANSGENDER PERCEPTIONS OF HEALTHCARE 147

A Pearson's product-moment correlation was run to assess the relationship between community
connectedness score and pride score. 213 participants were recruited, and preliminary analyses showed
the relationship to be linear with both variables normally distributed, as assessed by Q-Q plots in SPSS
version 28.0. There was a statistically significant, moderate positive correlation between community
connectedness score and pride score, r(213) = .56, p < .01, with community connectedness scores
explaining 32% of the variation in pride scores as shown in Figure 30 below.
Figure 30
Correlation of Community Connectedness and Pride

TRANSGENDER PERCEPTIONS OF HEALTHCARE 148

Abstract (if available)

Abstract This study applies the Gender Minority Stress and Resilience (GMSR) from academic literature to understand primary healthcare behaviors and perceptions within the transgender community. The purpose of the study is to examine how GMSR explanatory variables shape transgender patient behaviors and perceptions related to primary healthcare utilization. Specifically, this study aims to examine what health seeking behaviors look like for transgender patients, how transgender patients describe their health seeking experiences, what the relationships between GMSR variables are, and which GMSR factors, if any, predict transgender patient attitudes and intentions toward primary care utilization. Using data collected from a University of Southern California Institutional Review Board (IRB) approved survey of 213 transgender and gender non-conforming participants, the GMSR model was tested to examine predictive power for health-seeking, health-receiving, perceived physical health, and perceived mental health. Additionally, 42 open-ended survey responses were assessed for thematic analysis. Findings from this study indicate that income, age, insurance coverage, and some GMSR variables are able statistically significant in predicting healthcare behaviors and perceptions. Notably, internalized transphobia, age, and income moderately predict a transgender individuals perceived mental health. Open ended survey responses highlighted gender identity concealment, financial insecurity, and conflation of mental illness and gender dysphoria as inhibitors to care but identified community connectedness as a potential healthcare support. The implications of this study highlight that gender minority stress, income, and insurance coverage for transgender people drive healthcare disparities in the LGBTQIA+ community. This study seeks to make public policy-based recommendations that would bridge the healthcare gap for transgender patients seeking primary healthcare.

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Asset Metadata

Creator Smoak, Sebastian Nicholas (author)

Core Title Transgender patients’ perceptions of healthcare: A study of gender minority stress and resilience factors in predicting healthcare behavioral intentions

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Degree Conferral Date 2023-05

Publication Date 04/21/2023

Defense Date 04/10/2023

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag community connectedness,gender discrimination,gender identity concealment,gender minority stress and resilience,health,LGBTQIA+,LGBTQIA+ health behaviors,OAI-PMH Harvest,predictive health factors,transgender

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor MacCalla, Nicole (committee chair), Hirabayashi, Kimberly (committee member), Kho, Adam (committee member)

Creator Email smoak@usc.edu,smoak6@yahoo.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113065090

Unique identifier UC113065090

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Legacy Identifier etd-SmoakSebas-11687

Document Type Dissertation

Format theses (aat)

Rights Smoak, Sebastian Nicholas

Internet Media Type application/pdf

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Source 20230421-usctheses-batch-1028 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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