A narrative systematic review of the gender inclusivity of measures of harmful drinking and their psychometric properties among transgender adults
Bibliographic record
Abstract
BACKGROUND AND AIMS: Experiencing higher rates of stigma, marginalization and discrimination puts transgender individuals at risk for alcohol use and associated harms. Measures of harmful drinking were designed with cisgender people in mind, and some rely on sex- and gender-based cut-offs. The applicability of these measures for gender diverse samples remains unknown. The present study had two aims: (i) identify gender-non-inclusive language and cut-offs in measures of harmful drinking, and (ii) systematically review research reporting psychometric properties of these measures in transgender individuals. METHODS: We reviewed 22 measures of harmful drinking for gendered language and sex- and gender-based cut-off values and provided suggestions for revision when warranted. We also conducted a systematic narrative review, including eight eligible studies, summarizing the psychometric properties of measures of harmful drinking in transgender populations. RESULTS: Six of 22 measures of harmful drinking were not gender inclusive, because of gendered language in the measure itself or use of sex- or gender-based cut-off scores. Only eight published studies reported psychometric data for these measures in transgender people. Apart from in one study, the Alcohol Use Disorders Identification Test (AUDIT) and Alcohol Use Disorders Identification Test Consumption (AUDIT-C) appear reliable for transgender adults (Cronbach's α: AUDIT [0.81-0.87] and AUDIT [0.72-0.8)]). There is initial support for using uniform cut-offs for transgender people for the AUDIT-C (≥3) and binge drinking (≥5 drinks in a sitting). CONCLUSIONS: Most existing measures of harmful drinking appear to be gender inclusive (containing gender neutral language and uniform cut-off scores across sex and gender groups) and some that are not easily adapted to be gender inclusive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".