Relationship Between Non-Affirmation of Gender Identity, Healthcare Stereotype Threat, and Perceived Employment Inequities

Relationship Between Non-Affirmation of Gender Identity, Healthcare Stereotype

Threat, and Perceived Employment Inequities

Section 1: Foundation of the Study and Literature Review

Introduction

Transgender and gender diverse (TGD) employees make up 18% (Baboolall et al.,

2022) of the staff in healthcare organizations across the globe. There are perceived

employment inequities surrounding employee retention rates, lack of diversity in staff,

and discrimination including sexual harassment and hostile work environments in the

healthcare workplace culture for TGD employees. Non-affirmation or rejection of

someone’s gender identity is a microaggression seen in the workplace by transgender and

non-binary (TGNB) employees (Parr & Howe, 2020). According to Thoroughgood et al.

(2020), TGNB employees face many issues at their workplace, including discrimination

and harassment leading to devastating emotional consequences. Because some healthcare

organizations focus only on the sexual orientation of minorities, there are high turnover

rates and low productivity for TGNB employees (Liu et al., 2022). The Equal

Employment Opportunity Commission (2021) has seen an uptick in discrimination claims

for the broader lesbian, gay, transgender, queer, plus (LGBTQ+) community over the past

several years. In FY 2021, EEOC saw over 1,989 complaints by LGBTQ+ workers,

compared to only 808 complaints in FY 2013. Employee retention and job satisfaction are

low for TGNB employees (Thoroughgood, 2020), hurting the bottom line for healthcare

organizations and preventing them from striving for more diversity in their workforce.

Healthcare stereotype threat is pervasive for the TGD community and is

increasing as anti-transgender laws affect healthcare progress across the United States.

Transgender individuals face higher rates of healthcare stereotype threat due to high rates

of daily stress and the lack of resources in healthcare (Saunders et al., 2023). TGD people

are forced to advocate for their needs to obtain affirming healthcare due to the systemic

stigma of being trans-masculine and non-binary (Seelman & Poteat, 2020). All of this

leans toward the idea that healthcare is not a system built for TGD patients requiring their

resilience to obtain resources, culturally competent care, and healthcare needs met.

The specific research problem that was addressed through this study is the high

rates of discrimination TGD employees face in their healthcare organizations and

interactions with human resources. The other research problem is the healthcare

stereotype threat for TGD patients when seeking care throughout the healthcare system.

The challenge in healthcare organizations is that even though more diversity, equity, and

inclusive (DEI) policies and procedures exist across organizations, environments with

microaggressions lead to more barriers to obtaining a more diverse workforce and are

reflective of the TGD patients who receive care. This study will increase the knowledge

surrounding the relationship between gender affirmation, healthcare stereotype threat, and

perceived employment inequities equipping healthcare practitioners and human resource

professionals with resources to support TGD employees and patients.

Other components of Section 1 include the background, purpose of the study,

research questions and hypotheses, theoretical framework, nature of the study, literature

review, definitions, assumptions, scope and delimitations, significance, and a summary

and conclusion. The literature review provided in this section is a brief review and

synthesis of related articles and studies.

Background

TGD employees face high rates of discrimination in healthcare organizations due

to many factors. Non-affirmation of gender identity is one of the microaggressions (Parr

& Howe, 2020) that TGD employees face when going to work. Gender-based

victimization occurs when employees cannot affirm, or there is non-affirmation of their

gender identity with the ability to pass as cisgender leaving TGD employees to feel

marginalized or treated differently from their peers. Victimization is a more significant

systemic issue surrounding gender and sexual minorities receiving healthcare and

employed in healthcare.

LGBTQ+ employees and patients face high rates of harassment dominated by a

perceived culture that is heteronormative and cisgender. DiPalma (2021) stated that there

is a shift in cultural acceptance for LGBTQ+, but there is a need for more improvement

for those living as TGD. While many employees know someone who identifies as

lesbian, gay, bisexual, or even queer (Baboolall et al., 2022), many do not understand the

lived experience of or know a TGD person, which leads to microaggressions and a lack

of understanding (Liu et al., 2022). This gap in lack of understanding leads to low

satisfaction, decreased productivity, and high turnover for TGNB employees. TGD

patients are on the receiving end of an ethos of lack of understanding surrounding TGD

identities coming in for preventive care and treatment affecting their mental and physical

health and creating harm (Koch et al., 2020). TDG patients must endure stigma and lack

of resources to access medical care.

This study is needed to increase the understanding and awareness of the TGD

community for healthcare organizations that provide care and employment. By increasing

the knowledge surrounding the relationship between gender affirmation, healthcare

stereotype threat, and perceived employment inequities, healthcare administrators,

clinicians, and allied health professionals will have more tools and resources to interact

successfully with the TGD community. These resources will better equip them to be more

empathetic and compassionate while bringing awareness to their internal bias that has

caused harm and trauma in their prior interactions with TGD patients and employees.

Problem Statement

The problems to be addressed are employee retention, lack of diversity, and

discrimination in the healthcare workplace environment for TGNB employees and

patients. Additionally, the results of this healthcare environment encompass the lack of

affirming healthcare concerning their gender identity and healthcare stereotype threat.

Non-affirmation of gender identity is a microaggression seen in the workplace by TGNB

employees (Parr & Howe, 2020) and patients in healthcare. Negative healthcare outcomes

in combination with the stress of mental harm create a barrier for TGD patients to find an

affirming provider (Kattari et al, 2020).

According to Thoroughgood et al. (2020), TGNB employees face many issues at

their workplace, including discrimination and harassment leading to devastating

emotional consequences. Because some healthcare organizations focus only on the sexual

orientation of minorities, there are high turnover rates and productivity for TGNB

employees. During COVID-19, the loss of jobs meant a loss of health insurance for

sexual minorities and ultimately no access to healthcare due to the cost associated with

having access to care (Woolton, 2024). Healthcare stereotype threat increases when a

TGD person loses their job because they no longer can access a resource like health

insurance. They face the choice of food and shelter versus gender-affirming care and

medication.

The Equal Employment Opportunity Commission has seen an uptick in

discrimination claims for the broader LGBTQ+ community over the past several years.

Employee retention and job satisfaction are low for TGNB employees (Thoroughgood et

al., 2020), which hurts the bottom line for healthcare organizations and diminishes the

healthcare organization from striving for more diversity in its workforce. The lack of a

diverse workforce diminishes the ability to understand TGD patient needs and experience

when seeking care increasing the likelihood of increased healthcare stereotype threat.

Although researchers have investigated this issue, there is very little or no

literature on the roles of non-affirmation of gender identity, employment, and healthcare

stereotype threat to TGD individuals. This gap in the literature was the focus of this study.

Purpose of the Study

The purpose of this quantitative, retrospective, quasi-experimental study was to

examine the relationships between non-affirmation of gender identity, healthcare

stereotype threat, and perceived employment inequities in U.S. adults. The independent

variable was the non-affirmation of gender identity. The dependent variable was

healthcare stereotype threat and perceived employment inequities.

Research Questions and Hypotheses

The following research questions and hypotheses guided this study: RQ1:

What is the relationship between non-affirmation of gender identity and

healthcare stereotype threat?

H01: There is no statistically significant relationship between non-affirmation of

gender identity and healthcare stereotype threat.

H11: There is a statistically significant relationship between non-affirmation of

gender identity and healthcare stereotype threat.

RQ2: What is the relationship between non-affirmation of gender identity and

perceived employment inequities?

H02: There is no statistically significant relationship between non-affirmation of

gender identity and perceived employment inequities.

H12: There is a statistically significant relationship between non-affirmation of

gender identity and perceived employment inequities.

Theoretical Framework

The theoretical framework that guided this study is the sexual citizenship theory

created by T.H. Marshall (Rosich, 2020). The three elements of the model are (a) civil

society, (b) political society, and (c) the State or social society. Theories of structural

justice, like sexual citizen theory, are a subset of Marshall’s theory of social citizenship

(see Figure 1). Sexual citizenship theory focuses on the rights of civil society, such as a

person's right to work and seek healthcare for those who identify as TGD.

Figure 1

Sexual Citizenship Theory

The logical connections between the framework presented and the nature of the

study include theories of structural justice that examine concepts of heteronormativity,

homonormativity, and transmisogyny (Rosich, 2020) that affect workplace climate (civil

society) for TGD employees and patients obtaining healthcare. TGD employees face a

range of discriminatory biases in the workplace resulting in demotion or loss of

employment. With the loss of employment, the consequences are the inability to function

fully as an adult in the United States, which is the framework of sexual citizenship theory.

Additionally, TGD patients confront a healthcare system that has a lack of resources, high

levels of bias, and not enough culturally competent providers also in the sexual citizen

theory framework.

Nature of the Study

To address the research questions in this quantitative, retrospective,

quasiexperimental study, simple linear regression analysis was used to investigate the

correlation between non-affirmation of gender identity, healthcare stereotype threat, and

employment. The quantitative approach was ideal for this study, as it allows the gathering

of secondary data, which can be used to analyze the relationships between variables using

simple linear regression analysis. The retrospective approach signals the data were done

before this study. The study’s independent variable was non-affirmation of gender

identity. The dependent variables were healthcare stereotype threat and employment. The

data included were gathered from the Inter-university Consortium for Political and Social

Research (ICPSR) at the University of Michigan. Their secondary data were

predeidentified to ensure confidentiality. Variables related to non-affirmation of gender

identity, healthcare stereotype threat, and employment were exported as an Excel sheet.

After export, data analysis was conducted using simple linear regression to assess

relationships between the three variables.

Search Strategy

The following databases were accessed to obtain relevant literature: Taylor &

Francis, Thoreau, PubMed, EBSCOHost, and The International Journal of Transgender

Health. All sources were English-only text to reduce translation bias and peer-reviewed

studies that presented empirical data regarding key variables. This review did not include

doctoral dissertations and conference proceedings, as peer-reviewed literature was

accessible for discussion. Keywords were healthcare stereotype threat, gender identity,

minority stress, human resource management, DEI (diversity, equity, and inclusion)

policies, training, and transgender affirming. The literature review includes peerreviewed

resources published between 2013 and 2024. Key variables of the study frame the

literature review.

Literature Review Related to Key Variables and/or Concepts

Nonaffirmation of Gender Identity in Healthcare and Employment

TDG people face discrimination when they show up to work in healthcare

organizations when their gender is not affirmed, or it is rejected (Perales et al., 2021).

Discrimination is pervasive in healthcare for the LGBTQ+ community (Blackwell et al.,

2020), spilling to LGBTQ+ clients receiving care. Discrimination includes not being

addressed with the chosen name and pronouns, being harassed about how they express

their gender, being passed over for a promotion, having their workload diminished, and

being fired from their job. Safety of their employment becomes paramount when

compared to job satisfaction.

Safety for the employed TGD population includes the ability to affirm their

identity through gender expression (Hughto et al., 2020). Hughto et al. (2020) pulled data

from the 2015 US Transgender Stress and Health Study, in which 288 transgender

individuals over the age of 18 completed an online survey with 81% identifying as

transmasculine and 18.8% as transfeminine with non-binary identities included in both

categories with 96% of the participants disclosing their gender identity. The participants

were broken up into regions of the United States, with the West at 31%, the South at

26.7%, the Northeast at 26%, and the Midwest at 15.6%. Researchers found a statistically

significant relationship between gender affirmation experiences and several mental health

symptoms. The study assessed gender identity as cisgender, male and female, transgender,

female to male and male to female, and non-binary identities such as gender non-conforming

and non-binary. The question was asked about their medical affirmation and social

affirmation regarding their gender identity. The study focused on TDG individuals'

discrimination they experienced in seven areas including healthcare and whether they were

affirmed by their gender identity when they received care. Researchers scored their anxiety,

stress, and depression symptoms with mixed-effects models displaying self-reports of harm

and suicidality from before and after (p<.001) the start of the gender affirmation process.

Researchers found a link between mental health and gender-affirming procedures

concerning stress (p <.001), anxiety (p = .01), and depression (p =.01). Hughto et al. found

that the ability to affirm and express gender identity decreased depression and suicidality for

TGD people. The findings indicate the need for gender affirmation for the TGD population

to improve mental health.

Gender expression is the way a person displays their gender through the use of

clothing, mannerisms, and their voice through pitch and resonance (Sawyer &

Thoroughgood, 2017). Gender expression is perceived by coworkers (Davidson &

Halsall, 2016) as either male or female by other staff members due to the societal view of

only two genders. The binary perception of gender can create cognitive dissonance for

fellow employees and providers who may or may not have ever met a TGD person. A

lack of understanding or negative beliefs surrounding TGD people creates an

environment of uncertainty and, quite possibly, a safety concern for the employees and

patients.

With the current political landscape, many misconceptions around the TGD

community affect the healthcare industry, spilling over into patient care and employee

rights (Witt & Medina-Martinez, 2022). TGD people face high rates of unemployment,

housing crisis, and poverty due to misconceptions around their gender identity, requiring

nurses to empathically understand their needs for appropriate and competent care

(Wichinski, 2015). Healthcare professionals, then, must be educated on the additional

needs of the TGD population. Education then expands the cultural competence of the

environment to support all who work and receive care. However, the personal biases of

coworkers and providers can impede relationships, increase hostility in the workplace,

and create an environment where patients receive subpar care.

Bias around TGD employees’ gender identity creates threats to autonomy.

Consistent threats around gender identity and not feeling seen or heard result in hiding

gender identity in the workplace (Van Laar et al., 2019). The threat of being “outed” as

TGD requires the employee to deny their very existence and struggle with internalized

transphobia increasing the need for mental health support to overcome their personal bias

(Flynn & Bhambhani, 2021). The TGD employee is left feeling a lack of belonging to the

healthcare organization.

Lack of belonging in the workplace leaves TGD employees vulnerable to harm.

Healthcare continues to struggle with employees' psychological safety, meaning

discrimination runs rampant, where many are bullied, harassed, and belittled (Siad &

Rabi, 2021), opening space for TGD employees and patients to feel less than and not

affirmed. The lack of belonging creates a toxic and harmful environment for fellow

workers and the patients they serve. The harm cycle continues with stereotype threat,

creating an environment that lacks safety and belonging.

Belonging in the workplace means affirming the identities of fellow healthcare

colleagues in an organization. A healthcare environment that shows hospitality for

belonging and acceptance provides LGBTQ people a space to flourish and be present

(Newman et al., 2021). All humans inherit the need for belonging, as seen in Maslow’s

hierarchy of needs. TGD employees and patients feel valued and respected in a healthcare

environment that celebrates belonging.

Gender Identity in Healthcare and Employment

TGD employees and patients enter the healthcare environment with past

challenges relating to their gender identity. To fully understand the complexity of the

challenges, it is essential to define gender identity. Gender identity describes the

relationship between oneself and feelings about how one sees their identity, which can be

different from their outward expression of gender (Davidson, 2016). Gender identity is

tied to how a person feels about their role in society and is separate from their attraction

or sexual orientation to another person. TGD individuals know that entering healthcare

means they may only have binary choices on how they identify.

Healthcare is driven by a binary system that has been on a false notion that every

patient is female or male. This theory is harmful to the TGD community leading to hurt

and harm by the medical community and a feeling of being othered by identities such as

non-binary or genderqueer. In contrast, DNA is not female or male but karyotypes XX,

XY, XXY, and more (Vincent, 2019). Pragmatic views of gender are not straightforward

and can be used as microaggressions against the TGD community. However, when

language is introduced to healthcare professionals that includes a broader view of gender

and even sexuality, those they interact with feel a sense of respect and feel valued.

TGD people see gender identity as a spectrum, not a linear line where a human is

either female or male. Transgender is an overarching umbrella term split between binary

terms like transman or transwoman and nonbinary terms such as non-binary, gender fluid,

agender, and more (Green & Mauer, 2015). Transgender means a person does not identify

with the sex assigned at birth (Dahlen, 2020). These designations or identities require

healthcare providers and employers to examine how the employee and patient’s outward

appearance may differ and require less gendered notions.

Gendered notions or societal norms of gender focus solely on the belief that a

person is either female or male and is based on visualizing genitalia at birth. As

researchers have noted, gender and sex are different (Rouse & Hamilton, 2021), with sex

being tied to hormones, external sexual organs at birth, and genetic sex. On the other

hand, gender speaks to how someone is seen in their culture and society at large. These

differences can cause problems in healthcare because healthcare treatment and billing see

patients and employees who receive insurance as binary, either female or male (Wagner et

al., 2022). The binary constructs of the healthcare society do not match the current culture

of industrialized nations, pushing workplace policy changes to support TGD employees

and patients.

Western cultures tend to lean towards the binary gender theory (Thorne et al.,

2019). However, many indigenous cultures revere and celebrate more than two genders

creating conflicts in the healthcare system. Indigenous tribes across the Americas

recognize the term “two-spirit.” The term represents gender identity and sexual

orientation without assimilating to Western theory (Robinson, 2020) and Indian culture;

gender variance identities such as hijra, zenana, koti, kinner, and thirunangai (Ghosh,

2022) describe other genders in a cisgender colonized society. The cultural discrimination

with these identities spills over into the healthcare system when employees and patients

who identify with any number of TGD identities are not respected and even discriminated

against, adding to the challenges that TGD people face when staying employed and

receiving adequate care in the healthcare organization.

Challenges TGD People Face in Healthcare

TGD employees bring to work the burden of health disparities while taking care

of patients and working to stay present. They carry the burdens of homelessness,

discrimination, isolation, lack of healthcare access, substance abuse, and mental health

concerns into the workplace. TGD employees face high unemployment rates and tend to

work in low-paid positions (Leppel, 2021), resulting in community-based trauma

concerning their employment. Furthermore, workplace environments have become a

social determinant of health (Sherman et al., 2021). For TGD people, walking into their

daily job brings constant fear and dread surrounding their employment status and benefits

of health insurance, and ultimately staying safely housed. Furthermore, health insurance

does not ensure TGD safety in the healthcare environment.

A problem the TGD community faces is that health insurance is inherently binary,

forcing TGD people to fit into either the female or male identity leaving no space for non-

binary identities. Research has identified discrimination around insurance coverage of

gender-affirming care (Lerner et al., 2021) and non-binary individuals are less likely to

have affirmation of their gender in healthcare spaces (Reisner & Hughto, 2019), resulting

in employees and patients being unable to self-identify with their insurance and then

identified instead with their gender assigned at birth. The secondary data analysis of the

2015 Transgender Trans Survey (Lerner et al., 2021) pointed towards discrimination by

healthcare providers as the reason for barriers to healthcare utilization by the TGD

population in the United States, with 96% of the 21,930 TGD participants identified as

US-born citizens. The study found 22% of survey participants did not utilize healthcare at

all. Researchers looked at the relationship between avoiding healthcare and cost, invasive

provider questions (p<.001), refusal to provide affirming care (p<.001), verbal abuse

(p<.001), and provider’s lack of education to provide care (p<.001). The result is fewer

wellness visits and care by medical providers, adding more health crises’ and a lack of

mental health care. Health insurance for the TGD community must include coverage of

primary healthcare, including needs such as gender-affirming hormone therapy. Lerner et

al. (2021) pointed out the need for better education for providers to provide better care to

the TGD community. For many TGD individuals, living as their true selves and safely in

society means needing to pass as binary, leaving non-binary individuals less likely to fit

into the binary culture.

To pass means to be undetectable to others as anything other than cisgender

female or male (Doyle, 2022) or in a heterosexual relationship (Dixon & Dougherty,

2014). Passing helps some TGD employees navigate the workplace environment by not

bringing unwanted attention to their existence. By showing up as a binary gender, which

is socially accepted, TGD employees face less paranoia at work (Thoroughgood et al.,

2017). With these cultural norms, gender non-binary employees are then expected to

show up or express their gender as either masculine or feminine by cisgender colleagues.

TGD patients are seen as abnormal whether they pass or not due to the psychopathology

of their identity (To et al., 2020). The requirement to pass leaves TGD individuals with

more trauma and feeling less likely to belong to a team or even an organization providing

care.

Trauma for TGD people can include past trauma in healthcare, social situations,

workplace discrimination, and violence. Health and well-being are associated with a lack

of affirmation around gender identity (Doyle et al., 2021) and perceived discrimination in

the healthcare environment resulting in fear of safety, unemployment, and substance

abuse of TGD employees (Owens et al., 2022). Trauma, combined with repeated daily

work stressors, requires TGD people to be able to be present and reliable in their work

duties with or without additional resources to support their success. The emotional and

mental toll can result in unemployment and possible suicidal ideations for TGD

individuals.

Gender dysphoria is common for many TGD persons as a social determinant of

health, resulting in depression, anxiety, and self-harm (King & Gamarel, 2021).

Employees suffering from gender dysphoria or gender incongruence create minority

stress due to misconceptions and persecution of TGD individuals, where more support

and acceptance are needed (Piegza & Główczyński, 2022). While not all TGD people

suffer from gender dysphoria, challenges happen for those who do and are not supported

by their healthcare organization. As negative attitudes toward TGD people increase,

higher rates of anxiety and depression are soon to follow, adding to healthcare costs and

missed time from work.

Attitudes Around the TGD Population

Studies have shown a history of bias across health organizations and academic

medical institutions concerning the transgender community. With this, healthcare

professionals can help to advocate for their fellow TGNB colleagues and the patients they

serve. Liu et al. (2022) reported that doctors could use their clout along with their

upstream power and privilege to ensure policy change to support TGNB individuals

working and seeking care in their healthcare facility. Furthermore, privileges that include

the broader healthcare field have protection against HR policies that are not inclusive.

Enders et al. (2021) challenged leaders to address microaggressions when they happen,

not only during a performance evaluation. Policy advocacy and gentle nudges are some

ways that physicians and healthcare professionals can speak up against harm seen by

those TGD employees they work beside daily. Because doctors are held in such high

esteem, they can effect change on local and national levels within their professional

membership.

Many healthcare professionals fear saying anything (Canvin et al., 2023) because

of the lack of understanding of the TGD community, leaving TGD employees and

patients vulnerable to microaggressions. Canvin et al. (2023) analyzed qualitative data

from healthcare professionals who worked with the TGD community in London.

Providers felt they had inadequate training, fears of getting things wrong, lack of

experience or understanding, and the need for more training. The research showed that

conducting training in a non-judgmental space equipped healthcare professionals with the

tools and language to support the TGD community and overcome bias.

Healthcare professionals come with their belief systems and biases, which

translate into strained work relationships, including the inability to communicate and

work (Kanamori & Cornelius-White, 2016) with TGD employees. Having a hostile

working environment where microaggressions are present can create a feeling of not

belonging to the department or the organization, adding to the stress of TGD colleagues.

Along with biases, religious beliefs can also be pervasive throughout healthcare

organizations.

In 2016, it was estimated that about 14.5% of Catholic for-profit and non-profit

healthcare organizations in the United States (Khaikin & Uttley, 2016) shy away from

supporting or caring for TGD patients in their communities. Lawsuits by transgender

employees in religious healthcare organizations have popped up across the country

(Meyer, 2016), giving those employers religious freedom over Section 1557 of the

Affordable Care Act. The rulings allow faith-based organizations the freedom to take

away gender-affirming healthcare treatment coverage by insurance leaving TGD

employees and patients unable to have care covered and leaving them vulnerable to not

fitting in or feeling like they are welcome and supported.

Secular-based healthcare organizations can create hostile work environments for

TGD employees, as well. In the southeastern United States, transphobia and racism are

increased by conservative and evangelical environments resulting in high unemployment

rates, poor healthcare, and fewer social resources for TGD people (Johnson et al., 2020).

Religious-based healthcare organizations create more unsafe psychological space for

TGD due to past trauma and harm by established conservative religions (Scott et al.,

2021). Healthcare TGD employees end up in a position of constant harm or face

unemployment along with a lack of feeling of belonging because of their gender identity,

and patients feel unsafe to receive treatment.

Fitting in or feeling like the belong requires a binary gender expression for TGD

employees in healthcare. TGD employees will often attempt to change their gender

expression to fit in with their coworkers (Van Laar et al., 2019). TGD people do this by

presenting more binary or more masculine/feminine, like their cisgender cohort.

Appearing as female or male can harm the emotional and mental well-being of those who

identify as TGD. Furthermore, it perpetuates the stereotype of the binary system in the

healthcare environment adding more triggers to the employees just trying to do their job

and patients needing access to healthcare.

Transphobia in Healthcare and Employment

In the healthcare environment, transphobia is the best predictor of harm in the

TGD community. Transphobia, or the biases of transgender identities, can keep healthcare

professionals from being willing to learn and support TGD people (Stroumsa et al.,

2019). Those biases or transmisia keep TGD employees and patients from feeling

respected and accepted by the healthcare organization. Furthermore, it fosters a

healthcare environment where only cisgender employees and patients are supported.

Biases around the TGD community continue the progression of harm in healthcare.

Biases in healthcare create high rates of discrimination against minority healthcare

students. TGD medical students’ fear of affirming or disclosing their identity creates a

hostile environment in teaching hospitals (Giffort & Underman, 2016), furthering the

cycle of harm in healthcare. In an environment where all students are coming to learn,

TGD students hide their identity to fit into the cisgender culture. Healthcare misses out on

opportunities to learn from those whose lived experiences add to patient experience and

help to move past binary notions.

Healthcare is intrinsically binary-focused, founded on the notion that people are

either female or male. The binary notion is problematic for all identities because TGD

and intersex people find themselves in a situation where belonging to a team or

department becomes complicated. The binary theory is deeply entrenched in our society

(Thoroughgood et al., 2020) and the healthcare institution. Because healthcare treatment

is solely focused on female and male anatomy TGD people feel less than or abnormal

because of their physical anatomy. In turn, bias around bodies that do not fit in the box of

females or males leaves healthcare providers stuck between their fears and biases while

attempting to treat patients with dignity and respect.

Healthcare leaders in organizations often lack education on how to provide

appropriate care for the TGD community, resulting in ongoing microaggressions fueled

by unconscious biases. Unconscious bias is a stronger indicator than the education of

healthcare providers around the TGD community (Stroumsa et al., 2019), leading to more

harm to TGD employees and patients. Two hundred twenty-three clinicians, with 50.6%

being internists, 22.4% being family physicians, and 26.9% being ob-gyn practitioners in

the Midwestern United States, were surveyed with a 59% response rate. While most of

them treated at least one TGD patient (n=111, 49.7%), they did not have additional

education, and if they did it did not always address biases around TGD patients.

Researchers focused on health education (p=.292), transphobia (p<.001), and experience

with TGD patients (p=.259). Researchers found statistical significance that education did

not equate to knowledge, transphobia predicted knowledge. Microaggressions such as not

affirming identities, the use of chosen names and pronouns, and the assumption that every

employee is cisgender create an environment that is not supportive of employees and

patients who are TGD, and those microaggressions can be on top of widespread systemic

biases. Education can help address health disparities, however, bias around gender

identity does not always dissipate (Stroumsa et al., 2019). To prevent the harm caused by

these microaggressions, it is crucial to establish specific organizational policies that

prioritize inclusivity and address unconscious biases. This study enforced the need for

more education and self-reflection on conscious and unconscious biases when working

with the TGD population.

Numerous healthcare organizations have acknowledged their deficiency in

policies to tackle systemic biases and are now urgently striving to implement measures

that support their marginalized employees, including those who identify as TGD. The

lack of policies across healthcare organizations intensifies harm because TDG employees

report the lack of guidelines and enforcement of Title VII of the Civil Rights Act of 1964

that protects gender identity (Kleintop, 2019). Being TGD includes medical, social, or

legal transition, and the lack of policies leaves these employees without protecting their

identity (Westbrook & Schilt, 2014). The lack of policies leaves organizations without the

means to have a culturally competent systemic environment protecting anyone who is not

white, male, cisgender, or heterosexual.

Affirmation of Gender Identity

Cultural Competency in Healthcare and Employment

Cultural competency, although widely discussed in systemic environments like

healthcare, extends beyond a one-time effort or a mere notion. Cultural competency

requires consistent learning and updating views. For an organization to be inclusive, it

must have a diverse pool of employees and additional competency through creativity and

innovation (Hossain et al., 2020). Because higher levels of management in healthcare are

centered on cisgender white identities, the priority has been less than desirable, leaving

TGD employees stuck in less-than-affirming working environments. Cultural competency

necessitates going beyond simply educating staff about gender identity, racism, sexual

orientation, and issues faced by other underrepresented communities.

To effectively adapt to evolving language and new concepts, cultural competency

must be continuously updated and sustained. Due to the rapid pace and constant demands

of healthcare environments, health professionals face challenges in both delivering

patient care and ensuring equity among their staff and patients (Willging et al., 2019).

Unfortunately, education often neglects the inclusion of gender identity and sexual

orientation (Galupo & Resnik, 2016). The stress of working in healthcare can block

wellintentioned staff from alleviating their personal biases requiring cultural competency

to be more than an hour, once-a-year education. Allowing more staff practice and

reflection helps to understand the need for self-affirmation of identities for those

employed and seeking treatment at a healthcare organization.

Policy in Healthcare and Employment

Implementing gender-affirming policies for TGD employees is essential to foster

inclusive employee practices and ensure comprehensive insurance coverage for the

healthcare needs of all employees. While there has been a growth of DEI initiatives

across organizations, workplace buy-in is a requirement for success (McGregor et al.,

2019). Healthcare culture mirrors U.S. culture regarding bias around racism,

homophobia, and transphobia (Enders et al., 2021) and requires organizations to have a

strategic plan for more inclusion and affirmation.

Enders et al. (2021) researched Mayo Clinic’s DEI plan with a qualitative

approach to gather data around a DEI framework within the organization. One hundred

sixty-two participants encompassed their Health Services Research department. The staff

worked out of three of the Mayo Clinics located in Rochester, MN, Phoenix, AZ, and

Jacksonville, FL. The study participants shared ideas and feedback on the plan which

includes a two-step approach of increasing the belonging of underrepresented employees

and increasing staff diversity (Enders et al., 2021). Workplace environment focused on

training be the most important (p=.04) and review nudges (p=.01). The plan had two

goals, increase an overall sense of belonging and overall diversity throughout the Mayo

Clinic (p≤ .05). While this approach may seem too simplistic, it is a start to deeper

conversation and curiosity on how to get more input from staff and leaders in a healthcare

organization. Reviewing policies and procedures is only sometimes the first step in

building a more affirming and inclusive organization.

While Title VII of the Civil Rights Act of 1964 serves as a foundational starting

point for reviewing employee policies, it falls short of adequately protecting TGD

employees within healthcare organizations. Title VII broadly covers discrimination based

on gender identity but does not include policies or steps the administration must take to

protect TGD employees (Elias, 2017). Leaders and HR managers are then left to devise

policies to protect TGD employees and all other employees from discrimination. If the

organization needs TGD employees in leadership or diversity in the workplace, TGD

people are included in the conversation where input is essential. Moreover, cisgender

leaders lose the ability to have the lived experience needed to comprehend TGD

employee experiences fully. Having a solid workplace culture around all identities can

help support those underrepresented.

While Title VII offers certain protections for TGD employees, the Americans with

Disabilities Act (ADA) provides a more comprehensive level of security. The ADA offers

shelter in public accommodations and entities like bathroom protection, gender

expression, and time off for medical needs surrounding gender dysphoria and

genderaffirming care (Szemanski, 2020). ADA compliance in healthcare facilities is a

federal act supplying broader protections for TGD employees. Having the ability to

support TGD employees with federal policies helps in the creation of a workplace policy

that affirms all identities.

Healthcare policy for TGD patients helps to ensure they receive the best holistic

care. The ability to receive affirming holistic care requires the need for policies that

protect TGD patients including policies that address discrimination, health insurance

coverage, gender-affirming hormones and treatment, mental health care, and access to

changing legal documents (Goldenberg et al., 2020, Perone, 2020). Additionally, the need

for a review of structural policies, staff education, and continued access to care needs to

be considered in holistic care (Goszkowicz & Davis, 2023). These require hospital

administration to draft and execute a safety plan for TGD patients entering their

healthcare facility. Starting with federal discrimination policies in the Affordable Care

Act (ACA), such as Section 1557, gives healthcare organizations a starting foundation for

enacting policies that support the TGD community. Section 1557 of the ACA prohibits

discrimination in healthcare based on sex including gender identity supporting the TGD

communities’ need for protection while the ADA offers protection for receiving

genderaffirming care in healthcare organizations.

Religious exemptions cause harm to the TGD community when seeking

healthcare at faith-based health organizations. The Religious Freedom Restoration Act

(RFRA) prioritizes religious preferences over discrimination around federal laws such as

Title VII or Section 1557 of the ACA (Blazucki, 2023). RFRA opens the door for

religious healthcare organizations to deny healthcare and deny employees jobs or

promotions to TGD people. Ultimately resulting in harm and additional health disparities

for those who identify as TGD.

Self-Affirmation

The capacity to validate one’s own identity, such as gender, serves as a valuable

asset for an employee and patient entering a healthcare organization. Being able to show

up as one’s true authentic self is the key to self-affirmation (King & Gamarel, 2021;

Doyle, 2022, Rood et al., 2017). The capacity to express one’s gender identity is a unique

journey to a consistent determinant of health. The ability to express that gender can factor

in bias, racism, and discrimination resulting in trauma for TGD people (Sevelius, 2013;

Reisner et al., 2016). Employees, patients of color, and those TGD who present nonbinary

can find themselves at the end of microaggressions and biases by those who do not

understand or accept identities outside of the binary genders. The potency of

selfaffirmation diminishes when the ability to express it is restricted or denied for TGD

individuals.

By endorsing the ability to self-identify, people demonstrate support and

celebration of the diverse identities present in healthcare. Transitioning is a process of

living as one’s true self (a) legally: changing a person’s legal name; (b) medically:

surgical, or through gender-affirming hormone therapy and care; and (c) socially: by

expression of clothing, voice, and mannerisms (Thoroughgood et al., 2020). Supportive

healthcare cultures accept and celebrate TGD transitions through policy and approval of

procedures by the employee’s health insurance (Huffman et al., 2021). Ensuring people

have a voice and a seat at the table guarantees that they are both heard and valued.

ERGs

Despite the organization's overall need for greater representation, Employee

Resource Groups (ERGs) should strive for increased diversity, specifically by including

more individuals from the TGD community. ERGs, which include TGD voices, have

been used in organizations to improve DEI procedures and culture to improve the

inclusivity of their staff (McNulty et al., 2018). Furthermore, one of the metrics in the

Human Rights Campaign Healthcare Index (2022) scores the organization on whether or

not the company has an LGBTQ+ ERG and a community council. The community

council and ERGs influence the healthcare organization for both employee culture and

patient care for those they serve. At the same time, allies advocate and support their peers.

Healthcare Stereotype Threat

The theories of minority stress and stereotype threat center around the detrimental

impact of bias in healthcare. Minority stress is pervasive in healthcare leading to health

disparities (Burgess et al., 2010) and is the framework that appears with incongruency

between sexual minorities and dominant culture. Healthcare stereotype threat judging a

patient based on perceived unhealthy lifestyles and inferior intelligence (Abdou et al.,

2016). Healthcare stereotype threat others those who identify as a sexual minority like the

LGBTQ+ community, creating a space of judgment and bias for TGD individuals. The

presence of healthcare stereotype threat has a profound impact on both patients and

employees within the healthcare environment.

The persistence of healthcare stereotype threat in the workplace goes unnoticed

and concealed by hospitals and healthcare administration, permeating throughout

organizations. Research shows that healthcare professionals have overt and covert biases

toward the TDG community (Nadal et al., 2016). The biases the authors identified

included explicit and implicit, which are not always apparent to the person committing

the microaggression. The unconscious behavior causes harm to the TGD person, and the

aggressor’s behavior may not be held accountable due to the fear and isolation TGD

people feel because of the injury. The race and income of the TGD community can further

intensify the healthcare stereotype threat.

Given the intersectionality of TGD individuals’ identities, race assumes a

prominent role in shaping the healthcare stereotype threat. Researchers have identified a

correlation between race, gender, and sexual orientation in communities of color (Thorpe

et al., 2022). Thorpe examined secondary data specific to sexual identity and minority

stress in healthcare. One hundred forty-two Black and biracial cisgender females living in

the United States were asked about gender conformity and sexual identity concerning

exposing their identities to healthcare providers. Out of the participants, 45.6% identified

as bisexual, and 40.8% identified as lesbian/gay. While this study did not specifically

focus on gender identity, researchers found a significant correlation between Black

cisgender women who presented more neutral or masculine and healthcare stereotype

threat (p=.04). Results showed those whose gender identity is gender non-conforming

have higher levels of stigma in accessing healthcare (p=.03). A missing data point in this

study was the absence of Black TGD voices. Being TGD and BIPOC poses an even more

significant threat of bias and discrimination due to historical stereotyping and

victimization in a healthcare environment. Numerous DEI initiatives within healthcare

organizations are presently addressing racism and income disparities through the lens of

healthcare stereotype threat.

In healthcare settings, unconscious bias often occurs, leading to detrimental

effects on both employees and ultimately impacting patients. Microaggressions and the

suppression of gender identity and sexual orientation increase triggers of TGD across an

organization (Thorpe et al., 2022). Employees are then forced to deal or cope with the

stereotypes and stigma of being TGD every time they enter their workplace and TGD

patients fear accessing care. This burden affects the person as well as the organization

with absenteeism, mental and medical crises, and eventually loss of job or demotion in

work. Healthcare organizations incur hidden costs related to the recruitment of additional

staff, educational efforts, reputation deficits in terms of diversity, and the presence of a

hostile work environment.

Race

Healthcare stereotype threat is a comprehensive theory that examines biases and

stereotypes, yet it becomes problematic with the intersections of identities of race and

gender. Diving deeper into microaggression theory, a reader notices that personal

interactions filled with one-off comments or microaggressions converge (Arayasirikul &

Wilson, 2019) on the TGD community during their time at work or access to healthcare,

creating a dangerous environment for their health and well-being. According to the

research of Arayasirikul and Wilson (2019), 38 transwomen 16-24 years of age in

Chicago and Los Angeles shared their introductory work experiences and how their

identities were perceived in the workplace. The mean age of participants is 20.95 with

34.2% identifying as Black, 13.2% identifying as White, 15.8% identifying as Asian

American/Pacific Islander/Native American, and 23.7% identifying as Latino. The

qualitative study found a significant intersection of transmisogyny as a structure of

oppression by forcing TGD to pass which requires a medical transition. Workers who

could pass as cisgender had better workplace experiences versus those who were early in

their transition and faced transmisogyny and microaggressions for staff and clients.

The term transmisogyny, coined by Julia Serano in the mid-2000s, defines the

intersectionality of the biases of “transphobia, misogyny, and racism” (Whipple, 2021)

and has become a way to highlight the stereotypes and struggles of black TGD identities.

Researchers (Arayasirikul & Wilson, 2019) identified the passing of the complex (Figure

2), which is a continuous loop of transition work of passing, and the discrimination that

comes from not passing. TGD people not only have to overcome stressors around

transphobia but systemic racism is seen across the healthcare organization, once again

requiring TGD individuals to bear the burden of being resilient. TGD resilience comes

with high levels of mental health challenges, with substance abuse leading to

unemployment within the TGD community.

Figure 2

The Passing Complex (Arayasirikul & Wilson, 2019)

Trans women of color are less likely to be hired, creating housing and mental

health encounters leading to unemployment and housing instability. In the social

hierarchy of TGD identities, BIPOC women and feminine transgender individuals face

more violence and homicide due to systemic racism and transphobia, leading to fewer

employment opportunities, abuse, and housing insecurities (Wesp et al., 2019). The cycle

of black and brown sexual objectification creates more harm and less ability for

employment or access to healthcare. The TGD person’s healthcare then suffers from the

lack of lived experiences of BIPOC TGD employees and the inability to support patients

in that demographic. The lack of employment opportunities, housing, and access creates

income insecurities for BIPOC TGD individuals.

Self-affirmation of race supports black and brown TGD employees and patients.

Positive encounters in healthcare settings, such as self-affirming race and gender, lower

stereotype threats for BIPOC individuals (Taber et al., 2016). Furthermore, TGD

employees in rural areas and southern communities are less likely to support Black

employees (Smart et al., 2020). Healthcare organizations, where race is celebrated versus

environments where racism is present have better job satisfaction and fewer mental health

crises. In cases where stereotype threat becomes deeply ingrained in healthcare culture, it

inevitably results in unemployment which leads to income loss.

Perceived Employment Inequities

TGD live in fear that they will lose their job due to their gender identity or

expression of their gender. Discrimination is a concern for gender minorities because of

their gender identity creating an unsafe environment to self-identify on resumes and job

applications (Cabacungan et al., 2019). Moreover, disabled, gender minority employees

fear isolation and stress due to exposing their gender identity at work (Dispenza et al.,

2019). The perception of how their gender identity will be embraced or rejected adds

another layer of concern surrounding their employment opportunities or status at their

current organization creating perceived employment inequities. How the TGD employee

is perceived by their co-workers is a daily stress carried throughout their employment

journey.

Healthcare environments may be culturally competent and still have room for

improvement with TGD employees. TGD employees reported perceptions around their

visibility and ability to be out as themselves brought concern and worry about their equity

in the healthcare workplace which aligned with concerns by their cisgender co-workers

about TGD employees (Katz-Wise et al., 2022). Updated policies and continuing

education must continue to address improvements around employment inequities. Growth

areas in healthcare are felt by all employees in the arena of diversity, equity, inclusion,

and justice.

The rise in hostile work environments, the enactment of anti-transgender

legislation, and past instances of discrimination all contribute to the challenges faced by

TGD people in coping with the stigma associated with their identity. Resilience among

those in the TGD community is consistently noted in research (Van Laar et al., 2017).

However, it does not remove the constant discrimination the TGD employee faces when

entering a work environment and patients needing and receiving care. TGD individuals

face daily misunderstandings about their gender identity and expression of gender. They

must justify names, pronouns, and how they dress, walk and talk. TGD folks of color are

forced to combat elevated levels of discrimination because of stereotypes and biases

rooted in racism within healthcare organizations.

Income

In addition to facing unemployment, TGD people encounter additional challenges

such as lower wages and mental health crises. Workers who identify as TDG have lower

wages than their cisgender and lesbian, gay, and bisexual counterparts, leading to poor

mental health (Owens et al., 2022). Minority stress puts TGD employees in a position of

feeling unstable in their low-paying job, further exacerbating the worker’s mental health.

Additionally, TGD individuals have seen higher rates of discrimination in the healthcare

environment (Reisner et al., 2015). Healthcare stereotype threat overshadows the TGD

population's employment experience while attempting to do their low-paying job. The

bias and employment inequity overshadowing creates stress that negatively impacts the

mental health of workers, perpetuating a cycle that increases the risk of job loss,

substance abuse, and various social determinants of health.

Having a high level of education does not necessarily translate into increased

employment opportunities, in so doing impacting the income of TGD employees. TGD

people in states where there are transgender rights have a better chance at having a job,

and TGD employees tend to have more education than their cisgender counterparts, but

laws drive employers when hiring and wages (Leppel, 2016). Researchers note that laws

propel employment and wages higher or lower for the TGD community regardless of

degrees and education, even though they have more. Additionally, income is wielded both

as a tool for and against TGD employees, further aggravating income instability across

the board.

Summary of Literature Review

TGD people face discrimination in healthcare which often spills over into their

employment in healthcare organizations (Perales et al., 2021; Witt & Medina-Martinez;

2022). Gender identity plays a large role due to the ability to affirm gender identity

(Davidson, 2016; Doyle et al., 2021; Hughto et al., 2020; Sawyer & Thoroughgood,

2017) through expression, names, & pronouns at work. Bias in healthcare also plays a

role in the discrimination of TGD people (Flynn & Bhambhani, 2021; Stroumsa et al.,

2019; Van Laar et al., 2019). All this leaves TGD employees with feelings about their

safety (Siad & Rabi, 2021) and belonging (Newman et al., 2021). For TGD employees,

the healthcare workplace can wind up being an unhealthy environment to thrive (Doyle et

al., 2021; King & Gamarel, 2021, Lerner et al., 2021; Sherman et al., 2021) due to the

cultural norm of being cisgender (Doyle, 2022; Thoroughgood et al., 2017, 2020; Van

Laar et al., 2019). Healthcare leaders and professionals can improve the healthcare

environment through education (Enders et al., 2021; McGregor et al., 2019), addressing

microaggressions (Arayasirikul & Wilson, 2019; Canvin et al., 2023; Stroumsa et al.,

2019, Thorpe et al., 2022), and policy changes (Elias, 2017; Kleintop, 2019; Liu et al.,

2022; Szemanski, 2020). Overall, discrimination and bias around gender identity require

further consideration and research as a means of addressing and improving the needs of

the TGD community in healthcare.

Definitions

Non-affirmation of gender identity is the inability to affirm someone’s gender

identity using a chosen name, pronouns, and recognition of gender (Reisner et al., 2020).

Healthcare stereotype threat is a framework that describes negative stereotypes

around healthcare evaluation, treatment, and diagnosis by clinicians based on one’s social

group (Saunders et al., 2023).

Perceived employment inequities is a term that refers to the feelings employees

have based on their employment status based on the biases of their social group resulting

in demotion and termination or inability to be hired (Thoroughgood et al., 2017).

Cisgender is a term that refers to people whose gender identity aligns with their

sex assigned at birth (Goldbach et al., 2021).

Gender diverse is a term for a person whose gender identity or gender expression

does not conform to society’s definition of binary male or female gender norms (Rusow

et al., 2022).

Heteronormative is a system and a viewpoint that heterosexuality and binary sex

are the only sexuality and gender identity held as the cultural norm for all family

dynamics (Goldbach et al., 2021).

Homonormativity is adding the privilege, like same-sex marriage, of heterosexual

ideas and norms onto the LGBTQI+ community (Stewart, 2020).

Transgender is a term that refers to people with a gender identity that does not

align with their sex assigned at birth (Goldbach et al., 2021) and serves as an umbrella

term for the broader community (Green & Maurer, 2015).

Transmisogyny targets the transgender community of color with the intersectional

view of transphobia and misogyny (Boe et al., 2020), focusing on sexualizing and

fetishizing those whose gender identity is female or feminine expressing (Serano, 2021).

Assumptions

The assumptions in the study were that TGD employees in healthcare described

the workplace environment as their experience, the ability to affirm their gender identity,

and significant components surrounding workplace culture without the fear of retribution.

Additional assumptions include correct data collection and input by researchers without

bias and employees sharing their experience without the fear of workplace retaliation.

The experiences were necessary to measure the relationship between healthcare

stereotype threat and non-affirmation of the TGD lived experience within healthcare

organizations.

Scope and Delimitations

The scope of the study was limited to generalizations across healthcare

organizations. Each healthcare organization may have different experiences and

workplace cultures. Healthcare stereotype threat validity may vary based on the response

rate of staff and the TGD community regarding the feeling of safety to disclose their

gender identity, received bias, and harm. Perceived employment inequities can be felt at

any organization, for any TGD employee, based on their specific workplace environment.

Furthermore, the data set focused on the TGD community and does not represent all TGD

people and their lived experiences.

Limitations

Limitations include references from multiple healthcare environments such as

hospitals, clinics, pharmacies, and healthcare billing offices but the data did not provide

specific job titles or categories within healthcare organizations. Other limitations include

all TGD identities not specified in the dataset that excludes non-binary identities

terminology outside of non-binary and genderqueer, which excludes many more gender

identities. The measures used to address the limitations were the generalizations for

broader healthcare organizations which included using job titles such as clinicians,

healthcare professionals, and administrators for commonality. Employment inequity is

also generalized based on responses that were the perceptions of TGD individuals

working in the United States not always specific to healthcare. Other measures include

using TGD to encompass binary and non-binary transgender individuals.

Significance

The results of this study could potentially contribute to an examination and

explanation surrounding the healthcare stereotype threat for the TGD community in the

healthcare organization when they disclose their gender identity. Previous studies such as

the U.S. transgender survey in 2015 and 2022 focus on whether the TGD employee was

fired or demoted from their job (Leppel, 2021) and focus on bias and discrimination the

community lived through on a broader scale without affirming their gender identity. The

study identified the significant relationship between healthcare stereotype threat in

healthcare environments with TGD employees and patients. As such, the study may

potentially provide better clarification of the harm TGD individuals face in healthcare and

other industries around the affirmation of their gender identity along with the ability to

live their true authentic lives. This potential implication of this study will increase

understanding and awareness for those in healthcare to be a beacon of support to the

TGD community they serve and employ.

Summary and Conclusions

TGD people are continuously discriminated against in the healthcare environment

(Casey et al., 2019). They cannot affirm their gender identity due to bias and stereotypes

in and out of work (Reisner et al., 2016; Van Laar et al., 2019). The purpose of this

quantitative retrospective quasi-experimental study was to examine the relationship

between non-affirmation of gender identity, healthcare stereotype threat, and perceived

employment inequities of TGD people in healthcare organizations. This study will fill in

the gaps in the current literature to extend knowledge of awareness and provide more

support to the TGD community in healthcare organizations. This section identified the

supportive literature review, the purpose of the study, and the research questions. The

following section will include the design and methodology that guide this study.

Section 2: Research Design and Data Collection

Introduction

The purpose of this quantitative retrospective quasi-experimental study was to

examine whether there is a relationship between the non-affirmation of gender identity,

healthcare stereotype threat, and perceived employment inequities concerning employees

in healthcare systems and organizations. The variables are non-affirmation of gender

identity, healthcare stereotype threat, and perceived employment inequities.

Nonaffirmation focuses on the gender identity of the employee and patient. Healthcare

stereotype threat sheds light on the gender and sexual orientation of the employee and

patient. Perceived employment inequities center around their status only as an employee.

While TDG people face higher rates of discrimination, this study focused on the

culture of healthcare organizations where TGD patients and employees face inequities,

verbal abuse, and sexual assault. Conducting this study shed light on employee policy and

the treatment environment in healthcare organizations that affect the status of TGD

employment and the care TGD patients receive. All variables were gathered from

secondary data collection from the TransPop survey. Collected data involved using

correlation analysis via Statistical Package for the Social Sciences (SPSS) for Windows

Version 29, and descriptive statistics were calculated.

This section includes the research design and rationale for choosing a quantitative

method of research, the methodology will be explained, threats to validity will be

assessed with the final summary of the findings, and the end of this section will discuss

research design and collection.

Research Design and Rationale

This retrospective, quasi-experimental, quantitative study was designed to

examine the relationship between the independent variable of non-affirmation of gender

identity, and the dependent variables of healthcare stereotype threat and perceived

employment inequities. Because the variables in this study are identified and measurable,

the quantitative method is applied to the purpose of the study. This retrospective,

quasiexperimental was used to establish cause and effect because the data has already

been used in previous studies where there has already been intervention (White &

Sabarwal, 2014). For the field of study, the retrospective, quasi-experimental quantitative

research design was chosen to establish possible relationships between TransPop study

variables. The correlational study is the most logical for these specific variables from the

dataset, for this study.

This retrospective, quasi-experimental, quantitative study used a secondary

dataset collection from a survey from 2016-2018 specific to the TGD community and

their perception of their employment and healthcare access. The quasi-experimental

design was used in this study, with no time or resource constraints.

The purpose of this study was to examine any relationships with the

nonaffirmation of gender identity, healthcare stereotype threat, and perceived

employment inequities which are numerically measured for the secondary data source.

Data were received from people living in the United States.

Methodology

Population

In the TransPop survey from 2016-2018, the combination of transgender and

cisgender created one dataset. The transgender sample was N=274 and the cisgender

sample was N=1,162 with a combined total of N=1,436 across the United States. The

secondary data were collected from the TransPop survey. In the United States, it’s

estimated that the TGD population of adults 18 years and older is about 1.3 million

people, .5% of the population (Herman et al., 2022). The term transgender is a person

who does not identify with the sex they were assigned at birth (Dahlen, 2020). Because

the non-binary and genderqueer population data has not always been captured in US

census data until recently, this number is an estimate. Using the CDC’s Behavior Risk

Factor Surveillance System (BRFSS) survey, they report 38.5% (515,200) identify as

transgender women, 35.9% (480,000) identify as transgender men, and 25.6% (341,800)

identify as nonbinary/gender nonconforming (Herman et al., 2022) across the United

States.

Sampling and Sampling Procedures for Data Collection

The focus of the study was on a random sample of TGD employees throughout

the United States in 2016-2018 This study examined the variables using statistical tests

based on the secondary source of the TransPop Survey focusing specifically on

nonaffirmation of gender identity, healthcare stereotype threat, and employment.

Correlational methods of research are intended to determine the intensity of the

connection between variables (Frankfort-Nachmias et al., 2021). The sampling strategy

that was deployed was probability sampling which was random with known non-zero

probability. This study aims to add gaps in the literature and expand knowledge about

TGD employees in healthcare.

The original study goal was to get a representation of the TGD population in the

United States focusing on health outcomes, behaviors, institutional discrimination, and

other health-relevant domains based on personal perception. The TransPop was the first

national probability sample in the United States. The study is a combined dataset of two

data sources with one being TGD-only respondents, and the other being cisgender

respondents. The sample weight applied to the cisgender population is 50x the TGD

population. Bias surrounding data collection was twofold: a) target characteristic-TGD

and b) target community-LGBT. Recruitment and screening were performed by Gallup

Inc. using two methods:

1. US Adults using random digit dialing on cell and landline phones were

contacted and asked if they identified as lesbian, gay, bisexual, or transgender.

2. Shifted to address-based sampling by mail or online survey where Gallup sent

out surveys based on address.

Sampling inclusion included 18 years of age, the minimum of sixth-grade

education, and English speaking. The exclusion was Spanish speaking (5% of

participants), fifth-grade education and lower, and youth 17 years old and younger based

on the inability to consent legally. Each caller was then asked if they identified as LGBT,

if yes response, they were asked their sex assigned at birth and gender identity.

Respondents who identified as transgender served as the transgender sample. Those who

did not identify as transgender served as the cisgender sample. Participants were placed

in regions where they lived. Those regions include New England, Middle Atlantic, East

and West North Central, South Atlantic, East, and West South Central, Mountain, and

Pacific. Consent was assumed by filling out the survey or responding to questions asked

to each participant. Due to the privacy of disclosure, participants were not asked to sign a

consent form. In the second phase, respondents received a $25 gift card if they completed

the survey online or $25 cash if they mailed the survey back to Gallup.

The TransPop dataset is publicly available on the ICPSR at the University of

Michigan website. The study protocol was reviewed and approved by Gallup IRB and

UCLA IRB. The study collaborated with Columbia University, the University of Texas at

Austin, the University of California at Santa Cruz and San Francisco, the University of

Arizona, Surrey University, UK, and the University College of London, UK. These

collaborations included academic and professional input and feedback from TGD leaders,

researchers, and healthcare professionals.

A G* Power analysis was performed by using G*Power software version 3.1.9.7.

Input included a medium effect size of 0.15, an alpha error of probability of 0.05, a power

of 0.80, and one predictor. Based on the calculations, the necessary sample size for each

separate variable with statistical significance was 55. The available number of

participants is 252, which is adequate to achieve statistical significance and detect a false

null. With these tests, the minimum sample will support or disprove the statistical

significance of the research questions. Statistical significance can also rule out the null

hypothesis in this study.

Instrumentation and Operationalization of Constructs

The main data set used in the study is the TransPop survey from 2016-2018,

published in 2021 by Ilan Meyer. The goal of the study was to provide researchers with a

sample of the transgender population including basic demographics and examine health

outcomes and institutional discrimination (Meyer, 2021). Data for this study were

obtained publicly through the ICPSR at the University of Michigan website where no

permission was needed. The data was extracted from the website into an Excel

document. Specific data focused on non-affirmation of gender identity, healthcare

stereotype threat, and perceived employment inequities by use of sexual identity, race,

and gender minority. This dataset expanded upon another dataset, the US Trans Survey of

2014, and validated scales of the TransPop survey that measured the identity, stress, and

health of the transgender population in the United States.

Non-affirmation of gender identity is the independent variable. The dependent

variable is healthcare stereotype threat and perceived employment inequities. Survey

participants were instructed to choose their level of agreement with statements of the

quantitative variables from a 5-point Likert scale, ranging from “strongly disagree” to

“strongly agree.” The variables were recoded using a discrete count from zero to the total

sum score amount for each variable.

Nonaffirmation of Gender Identity

The variable for non-affirmation of gender identity looks specifically at the

question surrounding the perception of gender and gender expression. Gender conformity

for TGD people includes perception of gender expression or physical appearance and

non-affirmation of gender identity by others. Participants were given four statements

using a 5-point Likert scale in which they had to state whether they “strongly disagreed,

disagreed, neither agree nor disagree, agreed, or strongly agreed” to the statements. The

original data set used 1 for “strongly disagree,” 2 for “disagree,” 3 for “neither agree nor

disagree,” 4 for “agree,” and 5 for “strongly agree.” Using a recoded sum score for each

survey participant between 0-24. Zero denotes the participant has the least affirmation

and 24 denotes the participant has a strong affirmation of their gender identity. Strong

affirmation means that the TGD person felt affirmed in their gender identity, gender

expression, pronouns were respected, could “pass” regarding the gender identity, and

whether they felt understood as a person.

Healthcare Stereotype Threat

When the participants sought out healthcare, judgment, bias, and negative

diagnosis during their visits were asked by the researchers. Healthcare stereotype threat

refers to discrimination against TGD participants based on their sexual identity, and

gender minorities including BIPOC. The sexual identities include sexual minorities other

than heterosexual, lesbian, gay, bisexual, queer, same gender loving, asexual, and

pansexual. Gender identities include trans men, trans women, and trans-GNB (gender

non-binary). Participants were given four statements using a 5-point Likert scale in which

they had to state whether they “strongly disagreed, disagreed, neither agree nor disagree,

agreed, or strongly agreed” to the statements. The original data set used 1 for “strongly

disagree,” 2 for “disagree,” 3 for “neither agree nor disagree,” 4 for “agree,” and 5 for

“strongly agree.” This variable of healthcare stereotypes will include healthcare

experiences using a recoded sum score for each survey participant between 0-16. Zero

denotes the participant with the least healthcare stereotype threat and 16 means strong

healthcare stereotype threat. Strong healthcare stereotype means the TGD patient felt

judged negatively, worried about negative health interactions, receives a negative

diagnosis when seeing a provider, and is a negative stereotype for the TGD community.

Perceived Employment Inequities

The perceived employment inequities variable spotlights the employment status

of the TGD employee on how often the employee was fired from their job, denied a job,

denied a promotion, or received a negative evaluation based on their perception.

Furthermore, the variable looks at the outcome of employment focusing on being fired,

denied a job or promotion, or receiving a negative evaluation for each survey participant.

The scale used for these statements is how often the employee was fired, denied a job or

promotion, or received a negative evaluation with 1 being “never,” 2 being “once,” 3

being “twice,” and 4 being “three or more times.” A recoded sum score between 0-6. Zero

denotes the participant had no employment issues and 6 means three or more issues such

as termination, being denied a job or promotion, or receiving negative evaluations. The

two variables being used to measure negative employment outcomes record the number

of times fired, demoted, etc. as 0, 1, 2, or 3 or more. Even though there is no true count

for people fired more than three times, these represent a relatively small portion of the

sample (around 10%). The survey questions were treated as quantitative variables by

simply adding the two questions of how often the employee was denied a job or

promotion, negative feedback, or fired to get a rough measure of negative employment

outcomes. This biased the results towards being more conservative since those with more

than three firings or demotions were coded as having no more than three.

Data Analysis Plan

The data were exported from Microsoft Excel with the statistical analysis done

using SPSS (version 29). Data exploration started by preprocessing the variables and

removing all missing data and outliers to have a clear and concise data set and to ensure

the validity of the findings. The examination of the data was restricted to specific

questions about being fired, terminated, and denied a promotion. Using SPSS, the data set

was cleared of any missing data to ensure reliability. Demographics of the sample include

adults 18 years and older who identify as transgender or gender non-binary.

The data analysis was completed using a simple linear regression analysis, to

answer both research questions. Simple linear regression was appropriate for this study

because it establishes if there is a relationship between one independent and one

dependent variable. In this case, the independent variable is non-affirmation of gender

identity, and the dependent variables are healthcare stereotype threat and perceived

employment inequities. These three variables were created for the analyses as follows.

Tables 2, 4, and 6 in Chapter 3 refer to the TransPop survey questions that contain

data for the variable of non-affirmation of gender identity, healthcare stereotype threat,

and perceived employment inequities. Participants were given these statements using a

5point Likert scale in which they had to state whether they “strongly disagreed,

disagreed, neither agree nor disagree, agreed, or strongly agreed” to the statements. The

original dataset used 1 for “strongly disagree”, 2 for “disagree”, 3 for “neither agree nor

disagree”. 4 for “agree” and 5 for “strongly agree”. Scores were recoded to range from 0

to 4, then added together for a single index, resulting in a recoded sum score for each

survey participant between 0-24 for non-affirmation, 0-12 for healthcare stereotype

threat, and 0-6 for perceived employment inequities. Zero denotes the participant strongly

disagreed with all statements, and the upper range denotes the participant strongly agreed

with all statements.

These linearized index values were then checked to make sure values were not

clustered and hence better represented as a binary outcome. Because each index showed a

wide distribution of answers, simple linear regression was an appropriate way to

determine whether non-affirmation of gender identity was a significant predictor of either

outcome because it compares two quantitative variables, as long as their relationship is

linear and the deviations between the ordinary least-squares regression line and the actual

observed values are homoscedastic and normally distributed.

The research questions and hypotheses guide this study are as follows: RQ1:

What is the relationship between non-affirmation of gender identity and

healthcare stereotype threat?

H01: There is no statistically significant relationship between non-affirmation of

gender identity and healthcare stereotype threat.

H11: There is a statistically significant relationship between non-affirmation of

gender identity and healthcare stereotype threat.

RQ2: What is the relationship between non-affirmation of gender identity and

perceived employment inequities?

H02: There is no statistically significant relationship between non-affirmation of

gender identity and perceived employment inequities.

H12: There is a statistically significant relationship between non-affirmation of

gender identity and perceived employment inequities.

The results were interpreted using a P value of <.05 to signify statistical

significance.

Threats to Validity

Internal threats to validity include a low population of TGD identities present in

the study. In the data collection, white cisgender-identified participants are the majority

when it comes to race and gender identity. Sampling bias can be seen in the marketing

and outreach of the survey to a specific population in two ways. The first way is

marketing explicitly to TGD individuals. The second way is marketing to the specific

community of LGBTQ+ persons. External threats in this study are the data from the

study was used to determine whether or not there are statistically significant results for

non-affirmation of gender identity, perceived employment inequities, and healthcare

stereotype threat of the TGD population from the TransPop data set. Recoding of

variables was needed to explain the sum score of the participants in the analysis of the

data set.

Ethical Procedures

Collecting data for populations such as the TGD community assists social science

research in improving the lives of TGD people and aiding in additional research

collection that helps to respect the autonomy of the TGD community (Castendea &

Smith, 2022). This study uses principles of the Belmont Report which focuses on the

ethical ideas of respect for persons, beneficence, and justice (Brothers et al., 2019). While

the Belmont Report was drafted to protect research participants, it misses the mark on

protecting minority communities and populations that have been harmed in the past by

the medical community (Friesen et al., 2017). The TGD community has a history of

avoiding medical care due to discrimination and harm creating a culture of violence and a

lack of safety for TGD people.

The IRB must review and approve all research by the researchers of the TransPop

survey and through the Inter-university ICPSR at the University of Michigan. However,

this data set is available publicly with no personal data attached to it. There is no need to

request the TransPop data set.

For this study, the threat of harm is low to none due to the analysis of secondary

data. To ensure the confidentiality of the participants, no data will be collected, and the

data used will remain confidential. The variables include only the sample size and the

demographics of gender, sexual orientation, race, and income keeping other identifiers

out of the analysis. While the original data collected did not contain personal information,

a data breach would not cause harm to those who participated in the study.

Furthermore, to protect from a data breach, all information from this study was

protected. Any paper documents will be locked in a secure, fireproof safe. Electronic

materials will be encrypted, and password protected, as well as protected by a VPN

(Virtual Private Network), in my possession at all times. After 7 years after the

completion of this study, all documents, paper and electronic, will be destroyed by ways

of shredding, permanent deletion of files, and reformatting of drives to ensure deletion.

Summary

The purpose of this retrospective, quasi-experimental, quantitative study was to

examine the relationships between non-affirmation of gender identity, healthcare

stereotype threat, and perceived employment inequities for TGD people in healthcare

organizations. The variables measured in this study using secondary data are obtained

from the TransPop Survey from the Inter-university ICPSR at the University of Michigan

website. A correlation analysis will be used to analyze data from the TransPop data set

using SPSS for Windows Version 29. Section 3 will display the results and findings of the

analysis of the TransPop data set.

Section 3: Presentation of the Results and Findings

Introduction

The purpose of this quantitative retrospective quasi-experimental study was to

examine whether there is a relationship between the non-affirmation of gender identity,

healthcare stereotype threat, and perceived employment inequities concerning employees

in healthcare systems and organizations. This section includes the data collection of the

data set, the results from the analysis, and the summary.

The research questions and hypotheses guide this study are as follows: RQ1:

What is the relationship between non-affirmation of gender identity and

healthcare stereotype threat?

H01: There is no statistically significant relationship between non-affirmation of

gender identity and healthcare stereotype threat.

H11: There is a statistically significant relationship between non-affirmation of

gender identity and healthcare stereotype threat.

RQ2: What is the relationship between non-affirmation of gender identity and

perceived employment inequities?

H02: There is no statistically significant relationship between non-affirmation of

gender identity and perceived employment inequities.

H12: There is a statistically significant relationship between non-affirmation of

gender identity and perceived employment inequities.

Data Collection of Secondary Data Set

The secondary data set was collected from the TransPop study (Meyer, 2021).

The data was pulled from the Excel document and SPSS data set from the Inter-university

ICPSR at the University of Michigan There were no discrepancies in the use of the

secondary data set from the plan presented previously in Section 2.

The focus of the study was on a random sample of TGD employees throughout

the US in 2016-2018. Recruitment and screening were performed by Gallup Inc. using

two methods. First, US Adults using random digit dialing on cell and landline phones

were contacted and asked if they identified as lesbian, gay, bisexual, or transgender. Then,

it shifted to address-based sampling by mail or online survey where Gallup sent out

surveys based on address. The total survey sample in both cisgender and TGD identities

over the age of 18 years old. The baseline descriptive and demographic characteristics of

the sample included age, gender identity, sexual orientation, race, and education.

The statistical assessment used to assess the hypotheses for each of the research

questions proposed was a simple linear regression of the variables. Simple linear

regression was chosen as an appropriate method to assess whether non-affirmation of

gender identity significantly predicted either outcome variable while adhering to the

statistical assumptions. Because the one outcome and one predictor variable all showed a

wide distribution of answers, it compared two quantitative variables, as long as their

relationship is linear and the deviations between the ordinary least-squares regression line

and the actual observed values are homoscedastic and normally distributed.

The procedures used to account for this analysis were due to the multiple factors

influencing healthcare stereotype threat and perceived employment inequities. There was

no basic univariate analysis, covariates, or confounding variables. Using SPSS, the

analyze option was used to review descriptive statistics and frequencies to create tables in

the software.

For gender identity, Tran woman or male-to-female (MTF) made up 43.2 % (115

individuals) with Trans man or female-to-male (FTM) at 28.6 % (76 individuals) and

Trans GNB or transgender non-binary at 28.2% (75 individuals; Table 1). The mean age

of respondents was 39 years old with the highest numbers identifying as heterosexual

(21.7%), bisexual (19.0%), and queer (18.3%; Table 1). In total, 73.2% of respondents

identify as White, 9.6% as Hispanic, 8.8% as Black, 4.2% as Asian, and 4.2% as Other

(Table 1).

Table 1

TransPop Demographics

Gender N Percent

Trans man (FTM)

28.6

Trans woman (MTF)

115

43.2

Trans GNB

28.2

Total

266

100.00

Age

266

Mean

39.3

Median

Std. deviation

16.9

Minimum

Maximum

Sexual identity

Frequency

Percent

Straight/heterosexual

21.7

Lesbian

8.7

Gay

8.4

Bisexual

19.0

Queer

18.3

Same gender loving

3.4

Other

8.0

Asexual spectrum

4.6

Pansexual

8.0

Total

263

100.00

Race

Frequency

Percent

White

191

73.2

Other

4.2

Black

8.8

Asian

4.2

Hispanic

9.6

Total 261 100.0

Note. Demographics of the participants in the TransPop study who identified only as

transgender and gender diverse.

Results

Descriptive statistics and related data visualizations were reviewed for the

variables included in RQ1 and RQ2 using SPSS analysis output. The sample included a

total of n=1436 samples; n=1162 were cisgender male and female and n=274 TGD

individuals. Of the 274 TGD individuals, 266 were found to be valid (i.e., had complete

data) for the data analysis (Table 1). Three new variables were created for the analyses,

described as follows.

Table 2 refers to the TransPop survey questions that contain data for the variable

of non-affirmation of gender identity. Participants were given six statements using a

5point Likert scale in which they had to state whether they “strongly disagreed,

disagreed, neither agree nor disagree, agreed, or strongly agreed” to the statements. The

original dataset used 1 for “strongly disagree,” 2 for “disagree,” 3 for “neither agree nor

disagree,” 4 for “agree,” and 5 for “strongly agree.” Scores were recoded to range from 0

to 4, then added together for a single index, resulting in a recoded sum score for each

survey participant between 0-24. Zero denotes the participant has the least affirmation

and 24 denotes the participant has a strong affirmation of their gender identity. Strong

affirmation means that the TGD person felt affirmed in their gender identity, gender

expression, pronouns were respected, could “pass” regarding the gender identity, and

whether they felt understood as a person.

Table 2

Nonaffirmation of Gender Identity Statements

Strongly

Disagree

Neither

Agree or

Disagree

Agree

Strongly

Agree

Total

I have to repeatedly

explain my gender

identity to people or

correct the pronoun.

(22.2%)

(18.4%)

(23.7%)

(17.3%)

266

(100%)

I have difficulty being

perceived as my

gender.

(25.6%)

(15.0%)

(16.2%)

(22.9%)

(20.3%)

266

(100%)

I have to work hard

for people to see my

gender accurately.

(22.9%)

(16.9%)

(14.7%)

(22.6%)

(22.9%)

266

(100%)

I have to be overly

masculine or overly

feminine in order for

people to accept my

gender.

(22.9%)

(19.2%)

(24.1%)

(14.7%)

266

(100%)

People don't respect

my gender identity

because of my

appearance or body.

(23.3%)

(18.0%)

(19.2%)

(19.9%)

(19.5%)

266

(100%)

People don't

understand me

because they don't see

my gender as I do.

(16.9%)

(16.2%)

(13.5%)

(28.9%)

(24.4%)

266

(100%)

Note. Questions participants were asked about the affirmation of their gender identity.

Verification of the six questions used in the construction of the index variable for

non-affirmation of gender identity do not suffer from multicollinearity, as indicated by

Variance Inflation Factors (VIFs) less than five. Table 3 shows the results of this test.

Table 3

VIF Table for Nonaffirmation of Gender Identity

Non -affirmation of gender identity

VIF

I have to repeatedly explain my gender identity to people or correct the

pronoun

2.09

I have difficulty being perceived as my gender.

4.94

I have to work hard for people to see my gender accurately.

4.11

I have to be overly masculine or overly feminine in order for people to accept

my gender.

2.14

People don't respect my gender identity because of my appearance or body.

4.05

People don't understand me because they don't see my gender as I do.

3.03

Note: VIF less than 5

Figure 3 shows that although many respondents reported no incidents of

nonaffirmation of gender identity, the remainder of the distribution is relatively

uniform.

Figure 3

Frequencies of Nonaffirmation of Gender Identity

Table 4 refers to the variable of healthcare stereotype threat. Participants were

given four statements using a 5-point Likert scale in which they had to state whether they

“strongly disagreed, disagreed, neither agree nor disagree, agreed, or strongly agreed” to

the statements. The original data set used 1 for “strongly disagree,” 2 for “disagree,” 3 for

“neither agree nor disagree,” 4 for “agree,” and 5 for “strongly agree.” Scores were

recoded to range from 0 to 4, then added together for a single index, resulting in a

recoded sum score for each survey participant between 0-16. Zero denotes the participant

with the least healthcare stereotype threat, and 16 means strong healthcare stereotype

threat. Strong healthcare stereotype means the TGD patient felt judged negatively,

worried about negative health interactions, receives a negative diagnosis when seeing a

provider, and is a negative stereotype for the TGD community.

Table 4

Healthcare Stereotype Threat Statements

Strongly

Disagree

Neither

Agree or

Disagree

Agree

Strongly

Agree

Total

When seeking

healthcare, I worry

about being

negatively judged

because of my gender

identity or sexual

orientation.

(11.7%)

(14.7%)

(13.2%)

(34.2%)

(26.3%)

266

(100%)

When seeking

healthcare, I worry

that evaluations of

me may be

negatively affected

by my gender

identity or sexual

orientation.

(11.3%)

(15.4%)

(14.3%)

(35.3%)

(23.7%)

266

(100%)

When seeking

healthcare, I worry

that diagnoses of

me/my health may be

negatively affected

by my gender

identity or sexual

orientation.

(11.7%)

(18.0%)

(17.7%)

(29.7%)

(22.9%)

266

(100%)

When seeking

healthcare, I worry

that I might confirm

negative stereotypes

about LGBT people.

(23.3%)

(22.6%)

(16.2%)

(22.6%)

(15.4%)

266

(100%)

Note. Questions participants were asked about when seeking healthcare.

Figure 4 shows the distribution of healthcare stereotype threat shows a peak

around 12, with many people reporting either no threat or maximum threat. The

participants in the 0 and 16 categories were those that answered every stereotype threat

question the same, with either “Strongly disagree” or “Strongly agree” on all four

questions.

Figure 4

Frequencies of Healthcare Stereotype Threat

Table 5 refers to the variable of perceived employment inequities. Participants

were asked about their employment status on how often the employee was fired from

their job, denied a job, denied a promotion, or received a negative evaluation based on

their perception. The scale used for these statements is how often the employee was fired,

denied a job or promotion, or received a negative evaluation with 1 being “never,” 2

being “once,” 3 being “twice,” and 4 being “three or more times.” Scores were recoded to

range from 0 to 4, then added together for a single index, resulting in a recoded sum score

between 0-8. Zero denotes the participant had no employment issues, and 6 means three

or more issues such as termination, being denied a job or promotion, or receiving

negative evaluations. The two variables being used to measure negative employment

outcomes record the number of times fired, demoted, etc. as 0, 1, 2, or 3 or more. Even

though there is no true count for people fired more than three times, these represent a

relatively small portion of the sample (around 10%). The survey questions will be treated

as quantitative variables by simply adding the two questions of how often the employee

was denied a job or promotion, negative feedback, or fired to get a rough measure of

negative employment outcomes.

Table 5

Perceived Employment Inequity Statements

Never

Once

Twice

Three or

Total

Since the age of 18, how often

were you fired from your job or

denied a job?

127

(47.7%)

(15.0%)

(13.2%)

(24.1%)

266

(100%)

Since the age of 18, how often

were you denied a promotion or

received a negative evaluation?

142

(53.4%)

(18.8%)

(10.9%)

(16.9%)

266

(100%)

Note. Questions participants were asked about their employment.

Figure 5 shows that although many respondents reported no perceived

employment inequities, the remainder of the distribution is relatively uniform.

Figure 5

Frequencies of Perceived Employment Inequities

Table 6 shows the sample size for analyses is 266 since all models include

nonaffirmation of gender identity as the main predictor. The average index for perceived

employment inequities is 2, with a median of 1, indicating that values are right skewed.

Index values range from 0 to 6. Healthcare stereotype threat and non-affirmation of

gender identity have means of 9.1 and 12.1, respectively, on scales from 0 to 16 and 24,

respectively. Standard deviations are relatively large given the means, indicating that

there is significant variation in participant responses.

Table 6

Average Respondent Experience of Variables

Histogram of Perceived Employment Inequities

Perceived Employment Inequities

Non-affirmation of Healthcare stereotype Perceived gender identity

threat employment

inequities

266

Mean

12.1

9.1

2.0

Median

Std. deviation

7.5

4.7

2.1

Minimum

Maximum

Note: Average respondent experiencing one such event of each variable.

Both research questions use simple linear regression analysis. The four

assumptions for simple linear regression are (1) a linear relationship between the

independent and dependent variables; (2) no non-linear pattern exists in the residuals

relative to any independent variable; (3) homoscedasticity through which the residuals

have constant variance at every point relative to any independent variable; and (4) the

residuals in the model exhibit multivariate normality.

RQ1: What is the Relationship Between Non-Affirmation of Gender Identity and

Healthcare Stereotype Threat?

Assumption 1

The first assumption of a simple linear regression is that the relationship between

the predictor and the outcome is linear. Therefore, for the first assumption, I needed to

check to make sure the relationships between non-affirmation of gender identity and

healthcare stereotype threat are best described by a straight rather than curved line.

The scatterplot of healthcare stereotype threat as a function of non-affirmation of

gender identity (Figure 6) shows a weak linear relationship that is increasing.

Figure 6

Scatterplot for Nonaffirmation of Gender Identity and Healthcare Stereotype Threat

Assumption 2

The second assumption states that the relationship between the predictor and the

errors should not show any curved pattern. Because the mean of the errors is zero by

construction, the correlation between the predictors and the residuals will always be zero.

But the relationship can still be curved if the model over- or under-estimates the outcome

for low values of the predictor but not for high values, or not for medium values.

Assumptions 3

The third assumption states that the relationship between each predictor and the

errors should be homoscedastic, meaning the error terms are of similar magnitude

regardless of the value of the predictor. Homoscedasticity is visually checked by looking

for a funnel shape to residuals, which would suggest that the model does a better job for

low values of the predictor than for high values, or vice versa.

Both of these assumptions can also be checked in the scatterplot shown in Figure

6 because the error terms are based on only one predictor. Therefore, if there is no curved

relationship between non-affirmation of gender identity and healthcare stereotype threat,

there will also be no curved relationship between non-affirmation of gender identity and

the error terms. Likewise, there is no funnel shape to the points shown in the scatterplot in

Figure 6, which indicates homoskedasticity. Hence, Assumptions 2 and 3 are satisfied.

Assumption 4

The fourth assumption is that the residuals show normality. This is shown through

a P-P or Q-Q plot, which shows the observed error terms, ordered from most negative to

most positive, compared to the expected values of error terms if they are normally

distributed. We visually inspect how close the points are to a straight line, which indicates

perfect normality. The P-P plot shows cumulative probabilities and is hence more

sensitive to deviations from normality in the center of the distribution. The Q-Q plot

shows quantiles and is hence more sensitive to deviations in the tails.

Both plots for the model predicting healthcare stereotype threat (Figure 7) indicate

that the residuals are normally distributed, shown by the adherence of the expected

normal distribution of the residuals to the observed errors.

Figure 7

Normal P-P and Q-Q Plots of Residuals—Healthcare Stereotype Threat

Note: Figures show the straight line for normality of residuals of the independent

and dependent variables.

To approach RQ1 (What is the relationship between non-affirmation of gender

identity and healthcare stereotype threat?), a simple linear regression analysis was

conducted to evaluate the prediction of healthcare stereotype threat from non-affirmation

of gender identity. The results of the linear regression analysis (Table 7-9) revealed

nonaffirmation of gender identity to be a statistically significant predictor of the model (p

.001), shown in Table 9. The regression coefficient [B = .281, 95% C.I. (.213, .349), p <

.001] associated with non-affirmation of gender identity suggests that with each

additional point on the 24-point composite scale (Table 2) indicating non-affirmation of

gender identity, the healthcare stereotype threat increases by approximately 0.287 units

on the composite scale ranging from 0 to 16 units (Table 4).

The R2 value of .496 (Table 7) associated with this regression model suggests that

the non-affirmation of gender identity accounts for 49.9% of the variation in healthcare

stereotype threat, which means that 50.1% of the variation cannot be explained by

nonaffirmation of gender identity alone. Based on these results, the null hypothesis, that

there is no association between the degree of non-affirmation of gender identity and

healthcare stereotype threat, can be rejected. This is support for the alternative hypothesis

that the intensity of non-affirmation of gender identity one experiences increases the

likelihood of healthcare stereotype threat.

Table 7

Model Summary for Nonaffirmation of Gender Identity and Healthcare Stereotype Threat

Model Summarya

Model

R Square

Adjusted R

square

Std. error of the

estimate

.499a

.202

.199

4.21281

a. Predictors: (Constant); Non-affirmation of gender identity

b. Dependent Variable: Healthcare stereotype threat

Table 8 shows that the model proposed predicting healthcare stereotype threat as a

function of non-affirmation of gender identity performs statistically significantly better

than the null model, which simply estimates healthcare threat as the sample mean (F =

66.642, p-value < .001).

Table 8

ANOVA Test for Nonaffirmation of Gender Identity and Healthcare Stereotype Threat

ANOVAa

Model

Sum of

squares

Mean

square

Sig.

Regression

1182.750

66.642

<.001b

Residual

4685.400

264

17.748

Total

5868.150

265

a. Dependent Variable: Healthcare stereotype threat

b. Predictors: (Constant); Non-affirmation of gender identity

Table 9 shows the average index for healthcare stereotype threat is 5.72. All else

equal, for each additional point of increase in the non-affirmation of gender identity

experienced by trans participants in the survey, health care stereotype threat increases by

0.281 points (t = 8.163, p-value < .001).

Table 9

Coefficients for Nonaffirmation of Gender Identity and Healthcare Stereotype Threat

Coefficientsa

Unstandardized

Coefficients

std. error

Standardized

coefficients

beta

Sig.

(Constant)

5.72

.490

11.673

<.001

Non-affirmation of

gender identity

.281

.034

.449

8.163

<.001

a. Dependent Variable: Perceived employment inequities

RQ2: What is the Relationship Between Non-Affirmation of Gender Identity and

Perceived Employment Inequities?

Assumption 1

The first assumption of a simple linear regression is that the relationship between

the predictor and the outcome is linear. Therefore, for the first assumption, I needed to

check to make sure the relationships between non-affirmation of gender identity and

perceived employment inequities are best described by a straight rather than curved line.

The scatterplot of perceived employment inequities as a function of

nonaffirmation of gender identity shows no relationship, linear or otherwise (Figure 8).

The first assumption is satisfied because the relationship is not of a higher order.

Figure 8

Scatterplot of Nonaffirmation of Gender Identity and Perceived Employment Inequities

Assumption 2

The second assumption states that the relationship between the predictor and the

errors should not show any curved pattern. Because the mean of the errors is zero by

construction, the correlation between the predictors and the residuals will always be zero.

But the relationship can still be curved if the model over- or under-estimates the outcome

for low values of the predictor but not for high values, or not for medium values.

Assumptions 3

The third assumption states that the relationship between each predictor and the

errors should be homoscedastic, meaning the error terms are of similar magnitude

regardless of the value of the predictor. Homoscedasticity is visually checked by looking

for a funnel shape to residuals, which would suggest that the model does a better job for

low values of the predictor than for high values, or vice versa.

Both assumptions can also be checked in the scatterplot shown in Figure 8

because the error terms are based on only one predictor. Therefore, if there is no curved

relationship between non-affirmation of gender identity and perceived employment

inequities, there will also be no curved relationship between non-affirmation of gender

identity and the error terms. Likewise, there is no funnel shape to the points shown in the

scatterplot in Figure 8, which indicates homoskedasticity. Hence, Assumptions 2 and 3

are satisfied.

Assumption 4

The fourth assumption is that the residuals show normality. This is shown through

a P-P or Q-Q plot, which shows the observed error terms, ordered from most negative to

most positive, compared to the expected values of error terms if they are normally

distributed. I visually inspected how close the points are to a straight line, which indicates

perfect normality. The P-P plot shows cumulative probabilities and is hence more

sensitive to deviations from normality in the center of the distribution. The Q-Q plot

shows quantiles and is hence more sensitive to deviations in the tails.

Both plots (Figure 9) for the model predicting perceived employment inequities

indicate that the residuals are leptokurtic, meaning that the error terms are more narrowly

distributed around zero than would be expected in a normal distribution. Despite this

deviation from the expectation, linear regression is fairly robust to violations of the fourth

assumption, provided that the first three assumptions hold (Freedman, 2005).

Figure 9

Normal P-P and Q-Q Plots of Residuals—Perceived Employment Inequities

To approach RQ2 (What is the relationship between non-affirmation of gender

identity and perceived employment inequities?), a simple linear regression analysis was

conducted to evaluate the prediction of perceived employment inequities (Table 5) from

non-affirmation of gender identity. The results of the linear regression analysis (Tables

10-12) reveal that non-affirmation of gender identity not to be a statistically significant

predictor of the model (p = .50), as shown in Table 12. The regression coefficient [B =

.012, 95% C.I. (-.022, .046), p = .495] associated with non-affirmation of gender identity

suggests that with each additional point on the 24-point composite scale (Table 2)

indicating non-affirmation of gender identity, I cannot say whether perceived employment

inequities will increase or decrease.

The R2 value of [0.042] (Table 12) associated with this regression model suggests

that non-affirmation of gender identity accounts for only 4.2% of the variation in

healthcare stereotype threat, which means that 95.8% of the variation cannot be explained

by non-affirmation of gender identity alone. The confidence interval of (-.022, .046) for

non-affirmation of gender identity associated with the regression analysis contains 0,

which means the null hypothesis, there is no association between the degree of

nonaffirmation of gender identity and perceived employment inequities, cannot be

rejected.

Table 10

Model Summary for Nonaffirmation of Gender Identity and Perceived Employment

Inequities

Model Summarya

Model

R Square

Adjusted R

Square

Std. Error of

the Estimate

.042a

.002

-.002

2.12867

a. Predictors: (Constant); Non-affirmation of gender identity

b. Dependent Variable: Perceived employment inequities

Table 11 shows the model proposed predicting negative employment outcomes as

a function of non-affirmation of gender identity does not perform statistically

significantly better than the null model, which simply estimates negative employment

outcomes as the sample mean (F = .467, p-value =. 495).

Table 11

ANOVA Nonaffirmation of Gender Identity and Perceived Employment Inequities

ANOVAa

Model Sum of df Mean F Sig.

Squares Square

1 Regression 2.114 1 2.114 .467 .495b

Residual 1196.251 264 4.531

Total 1198.365 265

a. Dependent Variable: Perceived employment inequities

b. Predictors: (Constant); Non-affirmation of gender identity

Table 12 shows the average index for perceived employment inequities is 1.91.

Non-affirmation of gender identity does not have a statistically significant impact in this

sample. Although the coefficient for non-affirmation of gender identity suggests that for

each additional point of increase in the non-affirmation of gender identity experienced by

trans participants in the survey, perceived employment inequities increase by 0.012 points

(t = .683, p-value = .495). However, because the p-value is quite large, it cannot be

determined whether non-affirmation of gender identity has a positive, negative, or no

impact on perceived employment inequities.

Table 12

Coefficients for Nonaffirmation of Gender Identity and Perceived Employment Inequities

Coefficientsa

Unstandardized

Coefficients

Std. Error

Standardized

Coefficients

Beta

Sig.

(Constant)

1.905

.248

7.694

<.011

Non-affirmation of

Gender Identity

.012

.017

.042

.683

.495

a. Dependent Variable: Perceived employment inequities

There were two emerging hypotheses from the data analysis. The results show that

more research is needed surrounding the TGD community when looking specifically at

perceived employment inequities. The question is not, “Is there discrimination,” but

rather, “What level of discrimination does the TGD community face versus their

cisgender coworkers?”

Summary

The purpose of this quantitative retrospective quasi-experimental study was to

examine whether there is a relationship between the non-affirmation of gender identity,

healthcare stereotype threat, and perceived employment inequities concerning employees

in healthcare systems and organizations. The exploratory data analysis was performed to

present the characteristics of the data sets.

The results of RQ1 (What is the relationship between non-affirmation of gender

identity and healthcare stereotype threat?) showed that non-affirmation of gender identity

is a statistically significant predictor of healthcare stereotype threat. The null hypothesis

(there is no relationship between non-affirmation of gender identity and healthcare

stereotype threat) was rejected. The alternative hypothesis (a relationship exists between

non-affirmation of gender identity and healthcare stereotype threat) was accepted. RQ2

(What is the relationship between non-affirmation of gender identity and perceived

employment inequities?) showed that non-affirmation of gender identity is not a

statistically significant predictor of perceived employment inequities. The null hypothesis

(there is no statistically significant relationship between non-affirmation of gender

identity and perceived employment inequities) was retained. The alternative hypothesis

(there is a statistically significant relationship between non-affirmation of gender identity

and perceived employment inequities) was rejected. Section 4 discusses the conclusions

of the study and the possible reasons for the outcomes described in this section.

Limitations of the research and the shortcomings that were encountered during the

investigation will also be described. The section then provides a discussion on the

recommendations for future research, theory, and practice. The final section is then

concluded with a summary of the completed study.

Section 4: Application to Professional Practice and Implications for Social Change

Introduction

The purpose of this quantitative retrospective quasi-experimental study was to

examine whether there is a relationship between the non-affirmation of gender identity,

healthcare stereotype threat, and perceived employment inequities concerning employees

in healthcare systems and organizations. The nature of this study was to address the

research questions to investigate the correlation between non-affirmation of gender

identity, healthcare stereotype threat, and employment using secondary data from the

TransPop survey. This study is significant in that it fills in the gaps in the current

literature to extend knowledge of awareness and provide more support to the TGD

community in healthcare organizations, creating positive social change.

Through the secondary data analysis that was conducted, RQ1 (What is the

relationship between non-affirmation of gender identity and healthcare stereotype threat?)

showed that non-affirmation of gender identity is a statistically significant predictor of

healthcare stereotype threat. RQ2 (What is the relationship between non-affirmation of

gender identity and perceived employment inequities?) showed that non-affirmation of

gender identity is not a statistically significant predictor of perceived employment

inequities.

Interpretation of the Findings

The data analysis findings expanded the literature review in Section 1. The major

themes found throughout the exhaustive literature review were as follows: TGD

discrimination in healthcare environments, gender identity affirmation, bias in healthcare,

cultural norms surrounding cisgenderism, and the need for more education. The current

literature identified in the literature review in Section 1 provided an overview of barriers

to TGD patients in healthcare organizations; however, there was little to no literature on

personal awareness focusing on non-affirmation of a patient and employees’ gender

identity and ways to prevent harm to TGD patients and employees in healthcare. The

results of this study expand the findings from prior studies in which it examined the

impact of affirmation of the gender identity of patients and employees in healthcare

organizations. The specific aspects have not been addressed in prior research. The focus

on healthcare organizations, through the TransPop data set, concerning TGD patients and

employees was due to the lack of research done. The conducted analysis adds to the

literature by identifying the relationship between the independent and dependent variables

in RQ1.

In terms of the theoretical framework, the study’s findings align with the Sexual

citizenship theory by rights of civil society, such as a person's right to work and seek

healthcare for those who identify as TGD. Through this model, healthcare organizations

can evaluate their current structure and processes, such as how TGD patients and

employees access affirming care and have an affirming workplace. Sexual citizenship

theory is applicable and can be used to make sense of the literature review and data

analysis. However, there is no statistical significance between the non-affirmation of

gender identity and perceived employment inequities, as shown in Figure 9.

The scope of the study allowed for analysis and interpretation of the data provided

in the secondary quantitative dataset. The findings showed no statistically significant

relationship between non-affirmation of gender identity and perceived employment

inequities; however, there was a statistically significant relationship between

nonaffirmation of gender identity and healthcare stereotype threat.

Limitations of the Study

The limitations of this study included data, analysis, and the variable chosen such

as non-affirmation of gender identity versus gender identity. The TransPop survey was

conducted from 2016-2018, in the United States, through Gallop. Disclosure and privacy

safety are always a concern in the TGD community. Additionally, many of the data

collection surveys rely on self-reported data from the TGD individual and therefore may

not always be accurate as there is a possibility for bias by the TGD individual due to prior

trauma and past discrimination. RQ2 examined the non-affirmation of gender identity and

perceived employment inequities but using this independent variable the results focused

strictly on TGD individuals. The use of gender identity as the independent variable in

RQ2 would have examined the relationship between cisgender and TGD employees

regarding perceived employment inequities with possible statistical significance.

The results of this study may not be representative of all TGD individuals across

the United States. The determinants of health may vary from state to state depending on

anti-transgender laws and acceptance. The potential for generalizability was considered

but well understood that TGD participants are not all treated equally or receive

acknowledgment for their gender identity across healthcare organizations, whether the

organization is culturally competent and affirming. This is due to conducting an initial

assessment to identify the participant’s safety in disclosing and sharing experiences as

both a patient and an employee. Data about cisgender individuals were not used in this

study even though they were a part of the original survey. The data relationship of the

TGD and cisgender community with perceived employment inequities could have

expanded understanding of the inequities faced by the TGD community.

Recommendations

In this study, non-affirmation of gender identity, healthcare stereotype threat, and

perceived employment inequities were examined from the TransPop data set.

Recommendations for further research include quality of care for the TGD community

when their identity is not affirmed, analysis of transwomen and transfeminine individuals

around affirmation in healthcare treatment and employment inequities, and ways

healthcare administration tackles healthcare stereotype threats concerning the TGD

community. There is a lack of research surrounding euphoria in the TGD community

when seeking care or employment in healthcare in affirming environments.

This study was hindered by secondary data analysis and constraints of questions

asked to the participants. Research can examine how LGTBQI+ organizations and their

administrators best serve the TGD using their best practices to springboard better

longterm solutions to health disparities, bias, and discrimination. Research using holistic

solutions to provide care and social support for the TGD community.

Additional research is needed that focuses on how healthcare organizations tackle

bias around the TGD community in the long term. There are plenty of examples of how

healthcare organizations are harming the TGD community but fewer that are creating safe

and affirming environments for the TGD community. Further research can examine ways

that affirming space has improved the health and well-being of the TGD community

through ongoing cultural competency training, policy changes, affirmation by senior

leadership staff, and acceptance of gender identity.

Implications for Professional Practice and Social Change

Due to the ongoing anti-transgender legislation that is sweeping across the United

States, healthcare professionals struggle to best serve their TGD patients. Some

healthcare organizations are faith-based (Khaikin & Uttley, 2016) adding challenges of

religious beliefs to overshadow the care needs of the TGD community. Based on the

review of literature as mentioned in section one and the literature review, theoretical

framework, and analysis developed by this research, the study recommends several

changes to professional practice. The following suggestions are aimed at improving the

overall healthcare environment for TGD patients and employees in the United States.

First, it is recommended that healthcare administrators and healthcare leaders

review policy around cultural competency, how biases are addressed, and how safe and

affirming the organization is. Education for all healthcare staff is a resounding theme in

the literature. Instruction recommendations are ongoing training on understanding bias

and intersectionality of identities, how to affirm the gender identity of all staff cisgender

and transgender, and trauma-informed care and support (Boot-Haury, 2023). While these

training courses are not intended to be a fix, they can serve as a foundation for social

change and provide safer spaces with empathy and compassion for all who receive care

and come to work. The training requires that all staff working in healthcare environments

receive training that helps them create social and cultural change in the organization.

In addition to these recommended changes, promoting spaces where patients and

employees can be their authentic selves and not have to conform to binary rules supports

the TGD community. This requires patients and their families, staff, and volunteers to

enter the healthcare environment as themselves. This authenticity requires medical

records to reflect their gender identity and sexual orientation. Additionally, it opens space

to treat the whole patient including their family, homelessness, and lifestyle, not just

symptoms. Understanding and support of the patient and staff is more than just their level

of status in the organization. It will require every staff member from the CEO down to

entry-level staff members to ensure they are asking for and honoring the identities of

patients and staff through advocacy for every person.

Finally, healthcare organizations could lead the charge of ensuring equity by

speaking up against laws based on misconceptions and myths surrounding the TGD

community. Connecting with state and local legislators as a proponent for laws that

support and ensure the TGD community. Additionally, being a vocal advocate of the TGD

community that they serve can be achieved by scoring high on the Health Equity Index

through organizations like the Human Rights Campaign.

Future recommendations for research could include looking at the relationship

between gender identity and perceived employment inequities. This study showed that

there is a statistically significant relationship between the non-affirmation of gender

identity and healthcare stereotype threat showing a correlation of discrimination in

healthcare. The difference between being cisgender and transgender can lead to whether a

person is hired or fired at their workplace requiring employers to take action to stop harm

in their workplace. Another recommendation for research could include gender identity,

age, and perceived employment inequities. As the TGD community continues to age, how

will that affect their job status and discrimination?

Conclusions

The review of literature, theoretical framework, and analysis from this study

establishes the relationship between non-affirmation of gender identity and healthcare

stereotype threat. The purpose of this quantitative retrospective quasi-experimental study

was to examine whether there is a relationship between the non-affirmation of gender

identity, healthcare stereotype threat, and perceived employment inequities that TGD

employees face in healthcare systems and organizations. The questions that were used to

address the research problem identified in the purpose of the study were as follows: RQ1:

What is the relationship between non-affirmation of gender identity and healthcare

stereotype threat?

RQ2: What is the relationship between non-affirmation of gender identity and

perceived employment inequities?

The reason the study was conducted, the key findings, interpretations, limitations

of the study, recommendations for future research, applications to professional practice,

and implications for social change have been reviewed.

Through the literature review, discrimination and bias around gender identity

require additional consideration with ways of addressing and improving the needs of the

TGD community in healthcare. The bias and harm from not being affirmed spill over into

the employment status of the TGD individuals working in healthcare organizations.

Through the secondary data analysis that was conducted, RQ1 (What is the

relationship between non-affirmation of gender identity and healthcare stereotype threat?)

showed that non-affirmation of gender identity is a statistically significant predictor of

healthcare stereotype threat. RQ2 (What is the relationship between non-affirmation of

gender identity and perceived employment inequities?) showed that non-affirmation of

gender identity is not a statistically significant predictor of perceived employment

inequities. Through the theoretical framework, this study’s findings align with the sexual

citizenship theory by rights of civil society in which a person's right is to work and seek

healthcare for those who identify as TGD. With this model, healthcare organizations must

examine the rights of their patients and employees ensuring that they are affirming to

everyone who enters their doors.

This study’s findings have impacts on positive social change as it relates to the

TGD and healthcare organizations. This study is significant in that it can help to identify

ways healthcare administrators can ensure the safety of their patients and employees

within the organization and reflect those they serve. Recommendations for further

research include how healthcare organizations tackle bias around the TGD community

long-term. Further research can examine ways that affirming space has improved the

health and well-being of the TGD community and the acceptance of gender identity for

all people.

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