Understanding daily and momentary drinking motives among transgender and gender diverse adults
Bibliographic record
Abstract
Background Transgender and gender diverse (TGD) adults may be at increased risk for alcohol use due to gender minority stress (GMS) and specific drinking motives.Aim The current study evaluated the relations between GMS and resilience, drinking motives, and alcohol outcomes among TGD adults.Methods Twenty-five primarily White TGD adults (7 transgender men, 7 transgender women, and 11 gender diverse) who were 19 years of age or older (mean: 32.60; SD: 10.82), lived in Canada, and drank regularly were prompted to complete surveys multiple times per day indexing drinking motives, GMS and resilience factors, contexts, and alcohol use and craving. Multilevel models examined predictors of each drinking motive and relations between drinking motives and alcohol outcomes at the daily and momentary levels.Results Days with increased GMS corresponded with increased social (p<.05) and conformity motives (p<.01). Days with increased resilience also corresponded with elevated social (p<.01) and conformity (p<.05) motives. Days with higher enhancement and social motives predicted increased alcohol use (ps<.01). All momentary motives were elevated when participants reported higher momentary resilience (p<.001). Significant associations between drinking motives, contexts, and alcohol craving were also evident.Discussion GMS and certain drinking motives may contribute to increased alcohol use and craving among TGD adults. Findings encourage the development of gender-inclusive interventions that address GMS and specific drinking motives to support a reduction in this population’s alcohol use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".