Objectives. To provide empirical evidence of the positive effects of exposure to inclusive language on trans employees’ well-being.

Methods. We leveraged unique data from a large Australian national survey of workplace diversity and inclusion (2020 Australian Workplace Equality Index Employee Survey), focusing on a subset of trans respondents (n = 453). We derived self-reported and aggregate-level measures of exposure to trans-inclusive language and created a multidimensional index of employee well-being. We examined their relationships using fully adjusted random-intercept multilevel regression models.

Results. We found strong, positive, and statistically significant associations between different indicators of exposure to inclusive language at work and trans employees’ well-being. These relationships were large in magnitude and emerged in the presence of an encompassing set of sociodemographic and workplace controls, including other markers of workplace diversity and inclusion (e.g., victimization experiences and identity disclosure).

Conclusions. Our results provide robust evidence indicating that efforts to foster inclusive language at work can yield substantial, positive effects on trans people’s feelings of belonging and inclusion, thereby contributing to their overall socioeconomic integration. (Am J Public Health. 2022;112(3):482–490. https://doi.org/10.2105/AJPH.2021.306602)

A wealth of research has documented that trans people—encompassing binary transgender men and women, nonbinary people, and agender people—are at comparatively greater risk of poverty and social exclusion, homelessness, and family estrangement1,2 and have a greater likelihood of developing mental health problems and disorders.3,4 The profound and multidimensional disadvantage experienced by trans people has been traced to the unique stressors facing this group, including stigma and discrimination stemming from deep-rooted hetero- and cis-normative social structures, environments, and day-to-day practices.5–8

These stressors extend also to the domain of work, from which trans people are routinely excluded.9 For instance, the odds of unemployment are 3.2 times greater among trans individuals than cisgender individuals in the United States,10 and 43% of trans people in Germany reported experiences of work-related discrimination in a 2-year period.11 In addition, studies have documented multiple barriers to career progression among trans people, including discrimination in recruitment and promotion, high turnover, workplace bullying and ostracism, and low job satisfaction.12–14 The precarious situation of trans employees has fueled recent academic interest in the factors that contribute to better and worse work-related experiences among this group. One important factor, and the focus of this study, is exposure to inclusive language practices.

Language is a powerful vehicle not only for individuals to express their gender but also to validate or invalidate other people’s genders. Gendered language is particularly salient to trans individuals’ sense of self, given the discordance between their assigned and correct genders.15 As they affirm their gender, many trans people change their first names or choose to be referred to with personal pronouns that differ from those corresponding to their sex assigned at birth.16 For example, a trans woman may begin using feminine (i.e., she/her) or gender-neutral (e.g., they/them/their) pronouns.

Misgendering and mispronouning are 2 important types of language-related microaggressions faced by trans individuals within the workplace and elsewhere. Misgendering occurs when people use gendered language that does not match how a trans person identifies (e.g., describing a trans man as a woman), whereas mispronouning occurs when people use incorrect personal pronouns to refer to a trans person (e.g., using he/his/him pronouns to refer to a binary trans woman).17,18 Research indicates that both misgendering and mispronouning are relatively common, particularly within the workplace.1,17,19

A small but growing body of research has pointed to the importance of exposure to inclusive language—across social settings and at different points over the life course—for the well-being of trans people. This research has proposed that misgendering and mispronouning foster feelings of disrespect, invalidation, dismissiveness, alienation, and dysphoria among these individuals.20 Some pioneering studies have begun to provide associated empirical evidence through the use of adolescent samples.21,22 For example, in their investigation of a US sample of 129 trans adolescents, Russell et al.22 found that depressive symptoms and suicidal behaviors decreased when these adolescents were allowed to use their chosen name in a greater number of settings (e.g., at home and at school).

Studies focusing on trans adults are relatively scarce. Most rely on qualitative methodologies,23–25 precluding examination of generalizable patterns. A notable exception is McLemore’s analysis of a US sample of transgender adults, which showed that frequency of misgendering was positively associated with psychological distress.26,27 In the workplace, more specifically, Huffman et al. analyzed survey data from a sample of 263 gender-diverse individuals in the United States.20 Their findings indicated that coworkers’ use of correct pronouns and titles contributed to trans employees’ perceptions of workplace support, and this in turn increased their job and life satisfaction. Although these studies collectively suggest a link between inclusive language and well-being, the degree to which exposure to trans-inclusive language affects the well-being of trans employees remains an open question.

To date, investigating these relationships empirically has been challenging owing to a scarcity of suitable data. Trans people are a small population group,28 and their numbers in population studies are usually insufficient for separate analysis. Furthermore, existing surveys often lack key information to identify trans respondents (e.g., sex assigned at birth and gender identity). In addition, few data sets collect information on both employees’ use of trans-inclusive language and workplace well-being. This study overcomes these issues by leveraging unique data from an Australian survey on diversity and inclusion within the workplace: the Australian Workplace Equality Index (AWEI) Employee Survey. These data offer a rare opportunity to empirically examine how exposure to trans-inclusive language is associated with trans employees’ well-being. On the basis of the reviewed theoretical and empirical literature, we expected to observe strong, positive associations between these 2 constructs.

Data for the AWEI Employee Survey are collected by Pride in Diversity, a program that is part of ACON Health, Australia’s largest not-for-profit lesbian, gay, bisexual, trans, and queer (LGBTQ) community health organization. The aim of this annual, repeated cross-sectional survey is to document the impact of LGBTQ inclusion initiatives on organizations and their employees.29 Our study is based on data from the 2020 survey, which were collected through an online questionnaire issued to employees within organizations that were either members of Pride in Diversity or chose to participate. Participation of individual employees was voluntary.

The initial sample comprised more than 31 000 individuals from 149 organizations across a wide range of sectors and industries. Given the focus of this study, we analyzed data only from employees who identified as transgender, nonbinary, or agender (or as a member of another nondetermined gender minority group) and who had completed the survey modules on well-being and exposure to inclusive language. This yielded an analytic sample of 453 employees across 104 different employers.

Measures
Employee well-being

Our outcome variable was a composite index of employee well-being constructed by combining respondent-reported information on different domains of workplace inclusion and belongingness. Respondents who identified as gender or sexuality diverse were asked to rate their degree of agreement with 7 statements on a scale ranging from 1 (strongly disagree) to 5 (strongly agree). The statements were as follows: (1) “I feel mentally well at work,” (2) “I feel safe and included within my immediate team,” (3) “I feel accepted for who I am,” (4) “I feel I can be myself at work,” (5) “I feel productive at work,” (6) “I feel engaged with the organization and my work,” and (7) “I feel a sense of belonging here.” More than 99.5% of respondents who reached this part of the questionnaire provided responses for each of the 7 statements.

The 7 scores were averaged into an index ranging from 1 to 5. For easier interpretation, the index was subsequently rescaled to range from 0 (worst possible employee well-being) to 100 (best possible employee well-being). Rescaling was accomplished through the following linear transformation: new index = (original index − 1) × 25. The resulting scale featured a remarkable degree of internal consistency (Cronbach α = 0.92). In addition, results from a principal component analysis provided strong evidence of unidimensionality, with only a single factor having an eigenvalue above 1 (eigenvalue = 4.13). This factor explained 69% of the variance, and all of the items were positively and strongly correlated with it (r = 0.74–0.88; for details, see the appendix, available as a supplement to the online version of this article at http://www.ajph.org). The scale’s mean was 71.45, and its standard deviation was 23.13.

Exposure to trans-inclusive language

Our key explanatory variables were 4 measures of the degree to which trans employees were exposed to trans-inclusive language at work. The first 2 indicators were based on trans respondents’ self-assessments. They were taken from survey items gauging trans employees’ level of agreement with 2 statements pertaining to their experiences at work: “People make an effort to use my personal pronouns” and “I have been deliberately misgendered last year.” Responses were made on a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree); a “not applicable” option was also included. The 2 variables entered the base models as a set of dummy variables. “Strongly disagree” was arbitrarily designed as the reference category to which each of the other response categories was compared. In additional models, for parsimony, these variables were recast as continuous-level variables, with an additional dummy variable denoting whether respondents fell into the “not applicable” category to preserve linearity.

The third and fourth indicators approximated the cultural climate concerning language use in the organizations where trans employees worked. To derive these indicators, we exploited the multilevel structure of the AWEI Employee Survey data, with employees nested within organizations. Specifically, we aggregated responses to 2 survey items posed to all cisgender heterosexual individuals who worked within the same organizations as our focal trans respondents. The 2 items prompted these cisgender heterosexual individuals to rate their degree of agreement with the following statements: “I would be comfortable using they/their/them personal pronouns for a non-binary person at work” and “I would be comfortable referring to a colleague by a different name or personal pronouns if they were affirming their gender (transitioning) at work.” Responses were made on a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). These aggregated, organization-level measures entered the models as continuous-level variables.

Control variables

Several variables were used as model controls to minimize the risk of omitted-variable bias. All models included measures of respondents’ gender identity (trans man, trans woman, nonbinary, agender, different identity, prefer not to say), age group (less than 24 years, 25–44 years, 45 years or older), educational level (degree, lower), culturally and linguistically diverse background (yes, no), Indigenous self-identification (yes, no), workplace location (urban, rural), job level (senior/executive, middle management, regular employee), employment sector (government, private sector, other), and organization size (less than 500 employees, 501–8000 employees, more than 8000 employees). Inclusion of more disaggregated measures of occupation and industry was not possible owing to small cell sizes. As is customary in research conducted in Australia, the culturally and linguistically diverse background and Indigenous measures were used to approximate race/ethnicity.

The small amounts of missing data on age (n = 4), Indigeneity (n = 5), and culturally and linguistically diverse background (n = 5) were addressed through mode imputation. Missing data on organization size (n = 63) were more prevalent and dealt with through inclusion of a dummy variable denoting missing cases.

Some of our models were further adjusted for variables capturing workplace diversity and inclusion factors (beyond exposure to inclusive language) that could affect trans employees’ well-being. These variables were based on items in which trans respondents rated their degree of agreement with several statements on a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The statements were as follows: (1) “I have been the target of unwanted jokes due to my gender diversity last year,” (2) “I have been the target of bullying/harassment due to my gender diversity last year,” (3) “I can freely use gendered toilets of choice without opposition here,” (4) “Most people I work with are aware of my gender diversity,” and (5) “There are people within the organization similar to me.” All of these variables also included a “not applicable” category.

For parsimony, these variables entered the models as continuous-level variables, with an additional dummy variable identifying respondents who fell into the not applicable category. Table A (available as a supplement to the online version of this article at http://www.ajph.org) shows descriptive statistics for all analytic variables.

Analytic Approach

We used a series of multilevel (random-intercept) regression models to examine associations between our outcome variable capturing trans employees’ well-being and our key explanatory variables capturing exposure to trans-inclusive language.30 These models fully accounted for the hierarchical nature of the AWEI Employee Survey data, with employees (level 1) nested within organizations (level 2), generating appropriate parameter estimates and standard errors. The models can be formally expressed as follows:

(1)Wio=αo+βLio+γCio+δDio+eio
where i and o represent individuals and organizations, W is an index of employee well-being, L is a given indicator of exposure to trans-inclusive language, C is a set of base control variables, D is a set of additional controls for workplace diversity and inclusion factors, α is a model intercept that is allowed to vary across organizations, e is an idiosyncratic error term, and β, γ, and δ are parameter estimates (i.e., model coefficients) to be estimated.

We initially estimated models including only the explanatory variables capturing exposure to trans-inclusive language—one at a time—and the base control variables (base models). We then estimated models including additional controls for workplace diversity and inclusion factors (augmented models). The 2 sets of estimates had advantages and disadvantages relative to each other. On the one hand, the base models were less likely to yield parameter estimates for the language variables that were downward biased owing to collinearity and overcontrolling because of the presence of the other workplace diversity and inclusion factors. On the other hand, the augmented models were less likely to suffer from omitted-variable bias caused by a failure to include important predictors of trans employee well-being.

In this section, we present the results of the random-intercept multilevel regression models. We first discuss the results of the base regression models and then the results of the augmented models.

Base Regression Models

Table 1 presents abridged results from our base regression models. Models 1 and 2 show the results for the explanatory variables capturing self-reported exposure to trans-inclusive language. Trans employees who perceived that their colleagues made an effort to use their personal pronouns reported greater levels of well-being (model 1). For example, on a scale from 0 to 100, there was a 43-unit difference in the well-being index between trans employees who strongly disagreed and those who strongly agreed with the relevant statement (b = 43.25; P < .01). Similarly, experiences of deliberate misgendering were negatively associated with trans employees’ well-being (model 2). For instance, there was a 20-unit difference in the well-being index between trans employees who strongly disagreed and those who strongly agreed with the relevant statement (b = −20.45; P < .01). Results of analogous base regression models introducing the trans-inclusive language variables in a continuous metric are presented in Table C (available as a supplement to the online version of this article at http://www.ajph.org).

Table

TABLE 1— Abridged Results of Base Models of Employee Well-Being: Australian Workplace Equality Index Employee Survey, 2020

TABLE 1— Abridged Results of Base Models of Employee Well-Being: Australian Workplace Equality Index Employee Survey, 2020

Variable b (95% Confidence Interval)
Model 1 (R2 = 0.23)
Coworkers use respondent’s pronouns
 Strongly disagree (Ref) 0
 Disagree 14.57 (4.05, 25.10)
 Neither agree nor disagree 20.30 (10.28, 30.33)
 Agree 29.07 (19.45, 38.69)
 Strongly agree 43.25 (33.37, 53.13)
 Not applicable 24.81 (15.63, 33.99)
Model 2 (R2 = 0.12)
Respondent was deliberately misgendered
 Strongly disagree (Ref) 0
 Disagree −4.23 (–10.73, 2.26)
 Neither agree nor disagree −11.67 (–19.35, –3.99)
 Agree −15.93 (–24.36, –7.50)
 Strongly agree −20.45 (–29.28, –11.61)
 Not applicable −6.34 (–12.19, –0.50)
Model 3 (R2 = 0.07)
Colleagues comfortable using “they” pronouns 12.92 (0.55, 25.30)
Model 4 (R2 = 0.07)
Colleagues comfortable using different name/pronouns 15.96 (0.54, 31.39)

Note. Employee n = 453, organization n = 104. Data are unstandardized coefficients from random-intercept regression models. Models differ in the measure used to approximate exposure to trans-inclusive language (model 1: coworkers use respondent’s pronouns; model 2: respondent was deliberately misgendered; model 3: colleagues are comfortable using “they” pronouns; model 4: colleagues are comfortable using different name/pronouns). All models were adjusted for base control variables (gender identity, age group, educational level, culturally and linguistically diverse background, Indigenous self-identification, workplace location, job level, employment sector, and organization size). For full sets of parameter estimates for the control variables, see Table B (available as a supplement to the online version of this article at https://www.ajph.org).

Models 3 and 4 show the results for the explanatory variables capturing aggregate-level, derived measures of exposure to trans-inclusive language. Trans employees’ well-being was higher when others in their organization reported being comfortable using they/their/them pronouns (model 3: b = 12.92; P = .04) and when their colleagues reported being comfortable using a different name or pronouns for a person who transitioned at work (model 4: b = 15.96; P = .04).

The magnitude of these relationships can be better grasped by visual inspection of the different panels in Figures 1 and 2. These figures plot predicted means in employee well-being across categories or values of the inclusive language variables. All panels reveal monotonic, or nearly monotonic, increases in trans employee well-being as exposure to trans-inclusive language increases. The magnitude of the effect appeared substantial.

The parameter estimates for the control variables are presented in Table B (available as a supplement to the online version of this article at http://www.ajph.org). Although these estimates are not of key interest to our research aims, they revealed that few other sociodemographic or employer variables significantly predicted trans employees’ well-being. As an exception, we found a positive association between a culturally and linguistically diverse background and employee well-being.

Augmented Regression Models

Table 2 presents the results of augmented models that included additional variables capturing non-language-related workplace diversity and inclusion factors. For parsimony and comparability, all of the new variables as well as the self-reported language variables were introduced in the models in a continuous metric.

Table

TABLE 2— Abridged Results of Augmented Models of Employee Well-Being Capturing Additional Workplace Diversity and Inclusion Factors: Australian Workplace Equality Index Employee Survey, 2020

TABLE 2— Abridged Results of Augmented Models of Employee Well-Being Capturing Additional Workplace Diversity and Inclusion Factors: Australian Workplace Equality Index Employee Survey, 2020

Variable b (95% Confidence Interval)
Model 5 Model 6 Model 7 Model 8
Exposure to trans-inclusive language (continuous)
 Coworkers use respondent’s pronouns 4.71 (2.55, 6.88)
 Respondent was deliberately misgendered −1.41 (–3.30, 0.49)
 Colleagues comfortable using “they” pronouns 13.62 (6.40, 20.85)
 Colleagues comfortable using different name/pronouns 16.72 (7.63, 25.81)
Workplace diversity and inclusion factors
 Respondent was the target of unwanted jokes −5.09 (–7.25, –2.94) −4.89 (–7.24, –2.54) −5.65 (–7.81, –3.50) −5.64 (–7.80, –3.49)
 Respondent was bullied/harassed at work −2.91 (–5.27, –0.55) −2.76 (–5.17, –0.34) −2.84 (–5.20, –0.48) −2.83 (–5.19, –0.47)
 Respondent can use gendered toilets of choice 2.27 (0.59, 3.95) 3.54 (1.94, 5.14) 3.37 (1.79, 4.95) 3.41 (1.83, 4.99)
 Respondent is “out” to coworkers 3.73 (2.33, 5.13) 4.63 (3.31, 5.95) 4.59 (3.30, 5.87) 4.59 (3.31, 5.88)
 There are similar people within the organization 4.76 (3.18, 6.35) 5.18 (3.57, 6.78) 5.26 (3.68, 6.84) 5.21 (3.63, 6.79)
Overall R2 0.43 0.41 0.42 0.42

Note. Employee n = 446, organization n = 104. Data are unstandardized coefficients from random-intercept regression models. Models differ in the measure used to approximate exposure to trans-inclusive language (model 1: coworkers use respondent’s pronouns; model 2: respondent was deliberately misgendered; model 3: colleagues are comfortable using “they” pronouns; model 4: colleagues are comfortable using different name/pronouns). All models were adjusted for base control variables (gender identity, age group, educational level, culturally and linguistically diverse background, Indigenous self-identification, workplace location, job level, employment sector, and organization size). Parameter estimates for the “not applicable” categories are omitted for readability.

With the addition of the new variables, the parameter estimates for 3 of the 4 inclusive language variables remained statistically significant and in the expected direction. As an exception, the parameter estimate for the self-reported misgendering variable was no longer statistically significant in these models, which may have occurred because of misgendering being perceived as a form of bullying or harassment by trans people. Overall, this pattern of results suggests that the effect of exposure to inclusive language on trans employees’ well-being was independent of the effect of other diversity and inclusion factors that characterize workplace culture.

The parameter estimates for the additional workplace diversity and inclusion variables were largely consistent with expectations. As can be seen in model 5, for example, trans employees reported significantly greater well-being at work if they had not been the target of jokes (b = −5.09; P < .01) or bullied or harassed (b = −2.91; P = .01), if they could use gendered toilets of their choice (b = 2.27 P < .01), if they were “out” to their coworkers (b = 3.73; P < .01), and if they had colleagues who were similar to them (b = 4.76; P < .01). These relationships are visually represented in Figure A (available as a supplement to the online version of this article at http://www.ajph.org), which plots predicted means in employee well-being across values of the different diversity and inclusion variables based on the results of model 5.

This study has provided novel empirical evidence of positive effects of exposure to inclusive language among trans employees. Using unique survey data from Australia, we found strong, positive, and statistically significant associations between multiple markers of exposure to trans-inclusive language and trans employees’ well-being. These relationships emerged in the presence of an encompassing set of control variables and, in 3 of 4 cases, in models further adjusted for other workplace diversity and inclusion factors. Altogether, the results indicate that the positive effects of exposure to trans-inclusive language are not the product of confounding and operate independently of the effects of other workplace characteristics. Furthermore, these effects were more often statistically significant than those of the control variables (e.g., employee gender identity, ethnicity, or age and employer size, sector, or location) and were of a magnitude similar to that of other workplace diversity and inclusion variables with similar metrics (e.g., experiences of bullying and harassment).

Our findings are thus consistent with the results of a few earlier studies exploring relationships between inclusive language and trans people’s well-being in samples of adolescents21,22 and adults.20,26,27 However, this study was the first to our knowledge to quantify the direct impact of inclusive language use on a multidimensional measure of trans well-being in the workplace context. In addition, it was among the first to empirically demonstrate the contributing role of other workplace diversity and inclusion factors in the well-being of trans employees, including being the victim of jokes or bullying, being able to use gendered toilets of choice, being “out” to coworkers, and recognizing similar others within the organization.20

Strengths and Limitations

Our study featured several strengths stemming from the unique properties of the AWEI Employee Survey data. First, our analyses were based on the largest sample of trans people of all similar studies (n = 453). This sample size enabled us to examine the relationships of interest through complex multivariable regression models adjusted for a range of confounders within a multilevel framework. Second, we had access to 4 different measures of exposure to trans-inclusive language at work, including measures based on trans respondents’ self-reports and measures constructed out of aggregate reports from their colleagues. The fact that our results were consistent across all measures speaks of their robustness. Furthermore, we constructed and used a nuanced, multidimensional indicator of employee well-being captured via information from 7 items tapping different facets of the concept (e.g., safety, acceptance, productivity).

There were, nevertheless, some limitations to our research. First, the AWEI Employee Survey is a voluntary, opt-in survey for both employees and employers that relies on a nonprobabilistic sampling approach. As a result, any measures of statistical inference used in this study need to be interpreted with a degree of caution. This limitation is shared with other studies in the field,20,26,27 highlighting the need for future research to corroborate our findings with probabilistic samples. The organizations that chose to participate in the AWEI Employee Survey may feature more “progressive” workplace cultures than those that did not participate. Therefore, it remains unclear whether our results can be extrapolated to other organizations.

Second, because of data unavailability or modest cell sizes, we were unable to adjust our models for employee income; separate ethnicity, language background, and migrant status indicators; or highly disaggregated measures of occupation and industry sector. Finally, although exhibiting favorable statistical properties, the survey measures used in our analyses, including the focal explanatory and outcome variables, have to our knowledge not been formally validated.

Public Health Implications

Work-related experiences and outcomes are important determinants of health.31 As such, our results point to levers that employers and policymakers can use to improve the health and well-being of trans individuals. They indicate that efforts to foster inclusive language in the workplace can yield substantial, positive effects on trans people’s feelings of belonging and inclusion at work. Well-being at work is an important precursor of longer-term labor market outcomes such as employee retention and career progression, with potentially positive ramifications for outcomes in other domains of life.32 It follows that intervening to promote appropriate language use toward trans people at work may contribute to long-lasting positive effects on the lives of these individuals.

Current scholarship points to different interventions that can successfully change attitudes toward trans people and facilitate appropriate language use within the workplace. For example, increasing exposure to trans people reduces stigma and fosters appropriate workplace language practices.33,34 In addition to stronger antidiscrimination policies that boost employment rates among trans individuals, this goal can also be achieved through diversity and inclusion training that explicitly educates employees about the use of trans-inclusive language.35 Recent research indicates that some employee groups are less comfortable than others using trans-inclusive language at work (e.g., male, older, and less educated employees and employees working in male-dominated industries, rural areas, or the public sector).17 Hence, targeted programs aimed at these employee groups may be particularly efficient and effective.

See also Ross et al., p. 360.

ACKNOWLEDGMENTS

This research was partially supported by the Australian government through the Australian Research Council’s Centre of Excellence for Children and Families over the Life Course (grant CE200100025).

We are grateful to Maddy Donaghy for assistance in preparing the data set for analysis, to Alice Campbell for helpful comments and suggestions, and to the Pride in Diversity team (particularly Dawn Emsen-Hough, Jess Mayers, and Teddy Cook) for its feedback and support throughout the research.

CONFLICTS OF INTEREST

Francisco Perales and Christine Ablaza report no conflicts of interest. Nicki Elkin would like to disclose working at ACON Health (a not-for-profit organization committed to diversity and inclusion in the workplace), which collected the Australian Workplace Equality Index Survey data used in this study.

HUMAN PARTICIPANT PROTECTION

This study was approved by the ethics committee of the University of Queensland. Because the study was based on analyses of secondary data from a survey designed for research purposes, informed consent was not necessary.

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Francisco Perales, PhD , Christine Ablaza, PhD , and Nicki Elkin, MBA Francisco Perales and Christine Ablaza are with the School of Social Science, The University of Queensland, Brisbane, Queensland, Australia. Nicki Elkin is with Pride in Diversity, ACON Health, Sydney, New South Wales, Australia. “Exposure to Inclusive Language and Well-Being at Work Among Transgender Employees in Australia, 2020”, American Journal of Public Health 112, no. 3 (March 1, 2022): pp. 482-490.

https://doi.org/10.2105/AJPH.2021.306602

PMID: 35196034