Sex-for-Crack exchanges: associations with risky sexual and drug use niches in an urban Canadian city
Bibliographic record
Abstract
BACKGROUND: While crack cocaine has been associated with elevated sexual risks and transmission of HIV/STIs, particularly in the context of street-based sex work, few empirical studies have examined correlates of direct sex-for-crack exchanges. This study longitudinally examined the correlates of sex-for-crack exchanges and associated effects on sexual risk outcomes among street-based female sex workers (SW) who use drugs in Vancouver, Canada. METHODS: Data were drawn from a prospective cohort of street-based SWs (2006-2008), restricted to those who smoke crack cocaine. Multivariable generalized estimating equations (GEE) were employed to examine the correlates of exchanging sex for crack. A confounding model using GEE quasi-Poisson regression modeled the independent effect of exchanging sex for crack on number of clients/week. RESULTS: Of 206 SWs, 101 (49%) reported sex-for-crack exchanges over 18 months of follow-up. In multivariable GEE analyses, sharing a crack pipe with a client (aOR = 1.98; 95%CI: 1.27-3.08) and smoking crack in a group of strangers (e.g., in an alley or crackhouse) (aOR = 1.70; 95% CI: 1.13-2.58) were independently correlated with sex-for-crack exchanges. In our confounding model, exchanging sex for crack (aIRR = 1.34; 95% CI: 1.07-1.69) remained significantly associated with servicing a greater number (>10) of clients/week. CONCLUSIONS: These findings reveal elevated sexual- and drug- risk patterns among those who exchange sex for crack. The physical and social environment featured prominently in our results as a driver of sex-for-crack exchanges, highlighting the need for gender-sensitive multilevel approaches to harm reduction, STI and HIV prevention that address SWs' environment, individual level factors, and the interplay between them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".