Sexual identity and drug use harm among high-risk, active substance users
Bibliographic record
Abstract
Research shows that sexual minorities are at greater risk for illicit substance use and related harm than their heterosexual counterparts. This study examines a group of active drug users to assess whether sexual identity predicts increased risk of substance use and harm from ecstasy, ketamine, alcohol, marijuana, cocaine and crack. Structured interviews were conducted with participants aged 15 years and older in Vancouver and Victoria, BC, Canada, during 2008-2012. Harm was measured with the World Health Organization's AUDIT and ASSIST tools. Regression analysis controlling for age, gender, education, housing and employment revealed lesbian, gay or bisexual individuals were significantly more likely to have used ecstasy, ketamine and alcohol in the past 30 days compared to heterosexual participants. Inadequate housing increased the likelihood of crack use among both lesbian, gay and bisexuals and heterosexuals, but with considerably higher odds for the lesbian, gay and bisexual group. Lesbian, gay and bisexual participants reported less alcohol harm but greater ecstasy and ketamine harm, the latter two categorised by the ASSIST as amphetamine and hallucinogen harms. Results suggest encouraging harm reduction among sexual minority, high-risk drug users, emphasising ecstasy and ketamine. The impact of stable housing on drug use should also be considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".