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The Complexities of Categorizing Gender: A Hierarchical Clustering Analysis of Data from the First Australian Trans and Gender Diverse Sexual Health Survey

Denton Callander, Christy E. Newman, Martin Holt, Shoshana Rosenberg, Dustin T. Duncan, Mish Pony, Liadh Timmins Orcid Logo, Vincent Cornelisse, Liz Duck-Chong, Binhuan Wang, Teddy Cook

Transgender Health, Volume: 6, Issue: 2, Pages: 74 - 81

Swansea University Author: Liadh Timmins Orcid Logo

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DOI (Published version): 10.1089/trgh.2020.0050

Abstract

Purpose: This study used self-reported gender among trans and gender diverse people in Australia to identify and describe broad, overarching gender categories that encompass the expansive ways in which gender can be defined and expressed. Methods: Data were collected as part of the Australian Trans...

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Published in: Transgender Health
ISSN: 2688-4887 2380-193X
Published: Mary Ann Liebert Inc 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa64173
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Abstract: Purpose: This study used self-reported gender among trans and gender diverse people in Australia to identify and describe broad, overarching gender categories that encompass the expansive ways in which gender can be defined and expressed. Methods: Data were collected as part of the Australian Trans and Gender Diverse Sexual Health Survey hosted in October 2018. Participant self-identification with nonexclusive gender categories were analyzed using algorithm-based hierarchical clustering; factors associated with gender clusters were identified using logistic regression analyses. Results: Usable data were collected from 1613 trans and gender diverse people in Australia, of whom 71.0% used two or more labels to describe their gender. Three nonexclusive clusters were identified: (i) women/trans women, (ii) men/trans men, and (iii) nonbinary. In total, 33.8% of participants defined their gender in exclusively binary terms (i.e., men/women, trans men/trans women), 40.1% in nonbinary terms, and 26.0% in both binary and nonbinary terms. The following factors were associated with selecting nonbinary versus binary gender labels: presumed female gender at birth (adjusted odds ratio [aOR]=2.02, 95% confidence interval [CI]=1.60–2.54, p<0.001), having a majority of sexual and/or gender minority friends (aOR=2.46, 95% CI=1.49–3.10, p<0.001), and having spent more than half of one's life identifying as trans and/or gender diverse (aOR=1.75, 95% CI=1.37–2.23, p<0.001). Conclusion: Trans and gender diverse people take up diverse and often multiple gender labels, which can be broadly categorized as binary and nonbinary. Systems of health care and research must be adapted to include nonbinary people while remaining amenable to further adaptation.
Keywords: Cluster analysis, gender identity, health informatics, nonbinary
College: Faculty of Medicine, Health and Life Sciences
Issue: 2
Start Page: 74
End Page: 81

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