RETRACTED: Correlates of hazardous alcohol drinking among trans and non-binary people in Canada: A community-based cross-sectional study
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
PURPOSE: Transgender and non-binary people (TGNB) have a higher rate of heavy episodic drinking than cisgender people; however, extant knowledge about predictors of hazardous alcohol drinking (HAD) among different TGNB groups is limited. This study examined predictors of HAD in a national sample of TGNB people in Canada. METHODS: Logistic regression models were fit to examine the effects of 1) minority stressors and 2) stress-buffering factors on the likelihood of HAD, stratified by gender, among 2324 TGNB individuals from the Trans PULSE Canada survey, a cross-sectional survey conducted in 2019 among trans and non-binary people aged 14+ in Canada. RESULTS: Almost 17% of participants reported past-year HAD. Lifetime day-to-day and lifetime major discrimination were associated with higher odds of HAD in the full sample [(AOR=1.37, 95% CI: 1.30, 1.44) and (AOR=1.69, 95% CI: 1.55, 1.86) respectively], and across all gender groups. Social support was associated with lower odds of HAD in trans men, non-binary people assigned female at birth (NB-AFAB), and non-binary people assigned male at birth (NB-AMAB) groups, but with higher odds of HAD in the trans women group. Misgendering was associated with lower odds of HAD in trans men and NB-AFAB, but higher odds of HAD in trans women and NB-AMAB. Mixed effects of gender distress, gender positivity, and gender-affirming medical care were also reported across groups. CONCLUSION: The study provided a more detailed understanding of the predictors of HAD across four TGNB groups. Public health interventions should focus on structural discrimination and social support for TGNB people.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".