An Automated Algorithm for Classifying Expansive Responses for Gender Identity

被引:2
作者
Ceja, Alexis [1 ,2 ]
Raygani, Sawye [3 ]
Conner, Bradley T. [4 ]
Lisha, Nadra E. [2 ,5 ]
Bryant-Lees, Kinsey B. [2 ,6 ]
Lubensky, Micah E. [1 ,2 ]
Lunn, Mitchell R. [2 ,7 ,8 ]
Obedin-Maliver, Juno [2 ,8 ,9 ]
Flentje, Annesa [1 ,2 ,10 ]
机构
[1] Univ Calif San Francisco, Sch Nursing, Dept Community Hlth Syst, 2 Koret Way, San Francisco, CA 94143 USA
[2] Stanford Univ, Sch Med, PRIDE Study PRIDEnet, Stanford, CA USA
[3] Univ Calif San Diego, Sch Med, San Diego, CA USA
[4] Colorado State Univ, Dept Psychol, Ft Collins, CO USA
[5] Univ Calif San Francisco, Ctr Tobacco Control Res & Educ, Dept Med, Div Gen Internal Med, San Francisco, CA USA
[6] Northern Kentucky Univ, Dept Psychol Sci, Highland Hts, KY USA
[7] Stanford Univ, Sch Med, Dept Med, Div Nephrol, Stanford, CA USA
[8] Stanford Univ, Sch Med, Dept Epidemiol & Populat Hlth, Stanford, CA 94305 USA
[9] Stanford Univ, Sch Med, Dept Obstet & Gynecol, Stanford, CA USA
[10] Univ Calif San Francisco, Sch Med, Dept Psychiat & Behav Sci, Alliance Hlth Project, San Francisco, CA USA
关键词
gender identity; algorithm; sexual and gender minorities; methodology;
D O I
10.1037/sgd0000762
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Current two-step measures of gender identity do not prescribe methods for using expanded responses (e.g., multiple selections) among sexual and gender minority people, though they want the opportunity to provide these responses. To increase statistical power using expanded gender identity responses, we created an automated algorithm to generate analyzable categories. Participants' expanded gender identity responses and sex assigned at birth were used to create five categories (i.e., cisgender men, cisgender women, gender expansive individuals, transgender men, and transgender women) from a cohort of sexual and gender minority people (N = 6,312, 53% cisgender individuals). Data were collected from June 2020 to June 2021. Chi-square tests were performed to assess the association between the algorithm-generated and participant-selected gender categories and to identify demographic differences between participants in the algorithm-generated categories. Forty-six percent of our sample may have been classified into an "other" category without an algorithm due to writing their own response (5.7%), selecting "another gender identity" (5.7%), or selecting multiple (42.6%) or less commonly described (10.2%) gender identities. There was a relationship between the categories formed by our algorithm and participants' single category selection, chi(2)(20) = 19,000, p < .001. Concordance rates were high (97%-99%) among all groups except for participants classified as gender expansive (74.3%). Without an algorithm to incorporate expanded gender identity responses, almost half of the sample may have been classified into an "other" category or dropped from analyses. Our algorithm successfully classified participants into analyzable categories from expanded gender responses.
引用
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页数:12
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