Life Satisfaction and Negative Affect Among Trans Men, Trans Women, and Nonbinary Individuals in a U.S. National Sample.

Purpose: Transgender and nonbinary individuals often have limited educational and economic resources, lack social capital such as family and community support, and face discrimination. These factors are likely to have negative consequences for subjective well-being of transgender individuals. Yet, there is limited research using a national sample and comparing trans women, trans men, and nonbinary individuals. This study examined the impact of social support, social belonging, transgender connectedness, and discrimination on trans and nonbinary individuals' life satisfaction and negative affect. Methods: We used data from TransPop 2016-2018, the first survey conducted on a national probability sample of the transgender population in the United States. We focused on measures of life satisfaction and negative affect and their predictors, including social belonging, transgender connectedness, and everyday discrimination. Results: We found that trans men, trans women, and nonbinary individuals had lower life satisfaction and higher negative affect than cisgender heterosexual individuals. Social belonging had a positive effect on trans men and trans women's life satisfaction, whereas it had a negative effect on trans men and nonbinary individuals' negative emotion. While family support had a positive effect on trans men's life satisfaction, social support had mixed effects on nonbinary individuals' life satisfaction and negative affect. Finally, everyday discrimination had a negative influence on life satisfaction although there was variation by gender identity and dependent measure. Conclusion: Different factors predicted life satisfaction and negative affect of trans men, trans women, and nonbinary people. Thus, a one-size-fits-all model of trans and nonbinary subjective well-being does not work.

Will clinical standards not be part of the choir? Harmonization between the HL7 gender harmony project model and the NASEM measuring sex, gender identity, and sexual orientation report in the United States.

OBJECTIVES: To propose an approach for semantic and functional data harmonization related to sex and gender constructs in electronic health records (EHRs) and other clinical systems for implementors, as outlined in the National Academies of Sciences, Engineering, and Medicine (NASEM) report Measuring Sex, Gender Identity, and Sexual Orientation and the Health Level 7 (HL7) Gender Harmony Project (GHP) product brief "Gender Harmony-Modeling Sex and Gender Representation, Release 1." MATERIALS AND METHODS: Authors from both publications contributed to a plan for data harmonization based upon fundamental principles in informatics, including privacy, openness, access, legitimate infringement, least intrusive alternatives, and accountability. RESULTS: We propose construct entities and value sets that best align with both publications to allow the implementation of EHR data elements on gender identity, recorded sex or gender, and sex for clinical use in the United States. We include usability- and interoperability-focused reasoning for each of these decisions, as well as suggestions for cross-tabulation for populations. DISCUSSION AND CONCLUSION: Both publications agree on core approaches to conceptualization and measurement of sex- and gender-related constructs. However, some clarifications could improve our ability to assess gender modality, alignment (or lack thereof) between gender identity and assigned gender at birth, and address both individual-level and population-level health inequities. By bridging the GHP and NASEM recommendations, we provide a path forward for implementation of sex- and gender-related EHR elements. Suggestions for implementation of gender identity, recorded sex or gender, and sex for clinical use are provided, along with semantic and functional justifications.

Evaluating the Use of a Two-Step Gender Identity Measure in the 2018 General Social Survey.

In 2018, the General Social Survey (GSS) asked some respondents for their sex assigned at birth and current gender identity, in addition to the ongoing practice of having survey interviewers code respondent sex. Between 0.44% and 0.93% of the respondents who were surveyed identified as transgender, identified with a gender that does not conventionally correspond to the sex they were assigned at birth, or identified the sex they were assigned at birth inconsistently with the interviewer's assessment of respondent sex. These results corroborate previous estimates of the transgender population size in the United States. Furthermore, the implementation of these new questions mirrors the successful inclusion of other small populations represented in the GSS, such as lesbian, gay, and bisexual people, as well as Muslims, Buddhists, and Hindus. Data on transgender and gender-nonconforming populations can be pooled together over time to assess these populations' attitudes, beliefs, behaviors, and social inequality patterns. We identified inconsistencies between interviewer-coded sex, self-reported sex, and gender identity. As with the coding of race in the GSS, interviewer-coded assessments can mismatch respondents' self-reported identification. Our findings underscore the importance of continuing to ask respondents to self-report gender identity separately from sex assigned at birth in the GSS and other surveys.

Beyond the Castro: the role of demographics in the selection of gay and lesbian enclaves.

Although some qualitative research has noted differences in gay and lesbian enclaves based on characteristics such as race and sex, in this article, we draw upon quantitative data from the U.S. Census to demonstrate the manner in which enclave formation is affected by the interaction of sexual orientation and other demographic characteristics (such as sex, race, age, and income). We focus our attention on enclaves located in three counties in the San Francisco Bay Area: San Francisco County, Alameda County, and Sonoma County as one example. Even though these spaces fall within close proximity to one another and share similar geographic appeal, our analyses indicate that these enclaves are far from homogenous in terms of the demographic composition of their inhabitants. These quantitative analyses provide further support to past qualitative findings, as well as highlight additional distinctions in the manner in which demographics affect enclave selection. We supplement our demographic analyses with supporting field research and interviews, further highlighting both the variation and the commonalities of these enclaves. Overall, our findings promote an expansion of the understanding how intersecting demographic characteristics affect selection of a particular enclave and what may constitute a gay enclave.