“Am I gonna get in trouble for acknowledging my will to be safe?”: Identifying the experiences of young sexual minority men and substance use in the context of an opioid overdose crisis
Bibliographic record
Abstract
BACKGROUND: North America and other parts of the globe are in the midst of a public health emergency related to opioid overdoses and a highly contaminated illicit drug supply. Unfortunately, there is a substantial gap in our understandings about how this crisis affects key populations not conventionally identified within overdose-related surveillance data. This gap is particularly pronounced for gay, bisexual, and other men who have sex with men (sexual minority men)-a population that experiences substance use-related inequities across adolescence and young adulthood. METHODS: We draw on in-depth semi-structured interviews conducted in 2018 with a diverse sample (N = 50) of sexual minority men ages 15-30 who use substances and live in Vancouver, Canada, to identify how patterns and contexts of substance use are occurring in the context of the opioid overdose crisis. RESULTS: Our analysis revealed three themes: awareness, perceptions, and experiences of risk; strategies to mitigate risk; and barriers to safer substance use. First, participants described how they are deeply impacted by the contaminated illicit drug supply, and how there is growing apprehension that fatal and non-fatal overdose risk is high and rising. Second, participants described how procuring substances from "trustworthy" drug suppliers and other harm reduction strategies (e.g., drug checking technologies, Naloxone kits, not using alone) could reduce overdose risk. Third, participants described how interpersonal, service-related, and socio-structural barriers (e.g., drug criminalization and the lack of a regulated drug supply) limit opportunities for safer substance use. CONCLUSIONS: Equity-oriented policies and programming that can facilitate opportunities for safer substance use among young sexual minority men are critically needed, including community- and peer-led initiatives, access to low-barrier harm reduction services within commonly frequented social spaces (e.g., Pride, night clubs, bathhouses), nonjudgmental and inclusive substance use-related health services, the decriminalization of drug use, and the provision of a safe drug supply.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".