Exploring alcohol use and harm reduction techniques among transgender and gender diverse youth in Canada: A qualitative study
Bibliographic record
Abstract
Background: Compared to their cisgender peers, transgender and gender-diverse (TGD) youth and young adults report problematic drinking patterns (e.g. frequency of use, greater likelihood of use, and elevated risks for harmful use), as well as increased-risk of experiencing alcohol-related harms. Previous research suggests that increases in drinking among TGD populations may be motivated by experiences of gender minority stress (GMS). This study examines the lived/living experiences of Canadian TGD youth (ages 18-29), focusing on their experiences of GMS and resilience as it pertains to alcohol use, related harms, and harm reduction techniques.Methods: Semi-structured interviews with a peer researcher were conducted between November 2022 and May 2023, and in total, nineteen youth were interviewed. Questions focused on themes of GMS, resilience, and alcohol use. Data was coded and analyzed using an inductive thematic analysis approach.Results: Findings demonstrate that experiences of GMS (e.g. gender non-affirmation, discrimination, gender dysphoria, etc.), including difficulty accessing healthcare, was a primary drinking motivation among TGD youth. That is, alcohol was often used as a means to cope with GMS, and experiencing such stressors often similarly resulted in increased alcohol use.Conclusions: Our findings highlight the necessity of gender-affirming care, programming, and practices for TGD youth, as well as the important role of positive supports (e.g. family, community connections), all of which may serve to mitigate the heightened risks of increased alcohol use and related harms among TGD youth.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".