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Articles

What Is Needed, What Is Valued: Trans Students’ Perspectives on Trans-Inclusive Policies and Practices in Higher Education

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Pages 27-67 | Received 25 Aug 2017, Accepted 16 May 2018, Published online: 19 Jun 2018
 

ABSTRACT

This mixed-methods study of 507 trans and gender-nonconforming students (75% undergraduate, 25% graduate) aimed to understand (a) what institutional factors are associated with the presence of more trans-inclusive policies/supports, (b) what trans-inclusive policies/supports are viewed as important by different groups of trans students, and (c) how the presence of such policies/supports is related to trans students’ sense of belonging on campus and their perception of campus climate. Results indicated that religiously affiliated institutions and two-year institutions tend to lag behind in their inclusivity of trans students. Gender-inclusive restrooms, nondiscrimination policies that are inclusive of gender identity, and the ability to change one’s name on campus records without legal name change were among the supports that students valued most. Students articulated many concrete suggestions for institutions seeking to be more inclusive of their trans students. The known presence of trans-inclusive policies/supports was related to a greater sense of belonging and more positive perceptions of campus climate. These findings provide consultants and practitioners with guidance in identifying and promoting systems-level changes needed to support trans students.

Notes

1 Included here are the five participants who were intersex and assigned female at birth. In all analyses that used birth gender as a predictor, analyses were run both with and without intersex participants. Results did not change when these individuals were dropped from analyses.

2 We recognize that (a) masculine of center and feminine of center can be conceptualized as gender expressions and not gender identities, and (b) identifying as questioning is vague and does not clearly denote a binary or nonbinary identification. Thus, we carefully examined these participants’ endorsement of other identities to best categorize them. All but one of the individuals who identified as questioning, feminine of center, or masculine of center participants also selected one or more nonbinary identities (e.g., agender, nonbinary) and were categorized as such. One masculine of center participant also identified as a trans man and was categorized as binary-identified.

3 While several students use “preferred name,” this language is increasingly seen as offensive because “preference” indicates a choice, and other people do not have a choice in what name to use for a trans person. The same can be said for the use of “preferred pronouns.” Preferable language is “affirmed” names and pronouns.

Additional information

Funding

This work was supported by a Clark University Dean of Research grant.

Notes on contributors

Abbie E. Goldberg

Abbie E. Goldberg, PhD, is Professor of Psychology at Clark University. Dr. Goldberg’s research examines diverse families (e.g., LGBTQ parent families, adoptive families) and diversity issues in schools. She is the author of over 100 peer-reviewed publications on these topics, as well as several books, including Gay Dads: Transitions to Adoptive Fatherhood (NYU Press) and Lesbian and Gay Parents and their Children (American Psychological Association).

Genny Beemyn

Genny Beemyn, PhD, regularly writes, speaks, and consults on the experiences of trans college students and how campuses can develop trans-inclusive policies and practices. Their books include The Lives of Transgender People (Columbia University Press), A Queer Capital: A History of Gay Life in Washington, D.C. (Routledge), and Trans People in Higher Education (SUNY Press). They are the director of the Stonewall Center at the University of Massachusetts, Amherst and the Trans Policy Clearinghouse coordinator for Campus Pride.

JuliAnna Z. Smith

JuliAnna Smith, M.A., is an independent research methodolgist who specializes in the use of advanced statistical models, such as multilevel modeling and structural equation modeling, to analyze dyadic and longitudinal data.

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