Youth, violence and non-injection drug use: Nexus of vulnerabilities among lesbian and bisexual sex workers
Bibliographic record
Abstract
Despite increasing evidence of enhanced HIV risk among sexual minority populations, and sex workers in particular, there remains a paucity of epidemiological data on the risk environments of sex workers who identify as lesbian or bisexual. Therefore, this short report describes a study that examined the individual, interpersonal and structural associations with lesbian or bisexual identity among sex workers in Vancouver, Canada. Analysis drew on data from an open prospective cohort of street and hidden off-street sex workers in Vancouver. Bivariate and multivariable logistic regression were used to examine the independent relationships between individual-, interpersonal, work environment- and structural-factors and lesbian or bisexual identity. Of the 510 individuals in our sample, 95 [18.6%] identified as lesbian or bisexual. In multivariable analysis, reporting non-injection drug use in the last 6 months (adjusted odds ratio [AOR]= 2.89; 95% confidence intervals [CI]= 1.42, 5.75), youth ≤ 24 years of age (AOR= 2.43; 95% CI = 1.24, 4.73) and experiencing client-perpetrated verbal, physical and/or sexual violence in the last 6 months (AOR= 1.85; 95% CI= 1.15, 2.98) remained independently associated with lesbian/ bisexual identity, after adjusting for potential confounders. The findings demonstrate an urgent need for evidence-based social and structural HIV prevention interventions. In particular, policies and programs tailored to lesbian and bisexual youth and women working in sex work, including those that prevent violence and address issues of non-injection stimulant use are required. lesbian; bisexual; sex work; violence; youth; substance use Send correspondence
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".