Socioenvironmental and mental health determinants of alcohol use among Black sexually minoritized men and transgender women: Findings from the N2 cohort study
Bibliographic record
Abstract
Introduction: We aimed to identify socioenvironmental determinants of alcohol use among Black sexually minoritized men (SMM) and transgender women (TW). Method: Data were from the Neighborhoods and Networks (N2) cohort study among Black SMM and TW (01/2018-08/2020). We used stepwise negative binomial regression to identify associations with alcohol use. Result: =24±4 years), 68% reported past month alcohol use. Adverse childhood experiences (IRR=1.10;95%CI=1.03-1.18), GAD-7 (IRR=1.03;95%CI=1.00-1.07), and community violence (IRR=1.06;95%CI=1.02-1.10), were positively associated with alcohol use. Conclusion: Multilevel alcohol reduction interventions can consider reducing community violence and increasing access to mental health services.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".