The street cost of drugs and drug use patterns : relationships with sex work income in an urban Canadian setting.
Bibliographic record
Abstract
BACKGROUND: This study investigated the relationship between drug use and sex work patterns and sex work income earned among street-based female sex workers (FSWs) in Vancouver, Canada. METHODS: We used data from a sample of 129 FSWs who used drugs in a prospective cohort (2007-2008), for a total of 210 observations. Bivariate and multivariable linear regression using generalized estimating equations was used to model the relationship between explanatory factors and sex work income. Sex work income was log-transformed to account for skewed data. RESULTS: The median age of the sample at first visit was 37 years (interquartile range[IQR]: 30-43), with 46.5% identifying as Caucasian, 48.1% as Aboriginal and 5.4% as another visible minority. The median weekly sex work income and amount spent on drugs was $300 (IQR=$100-$560) and $400 (IQR=$150-$780), respectively. In multivariable analysis, for a 10% increase in money spent on drugs, sex work income increased by 1.9% (coeff: 0.20, 95% CIs: 0.04-0.36). FSWs who injected heroin, FSWs with higher numbers of clients and youth compared to older women (<25 versus 25+ years) also had significantly higher sex work income. CONCLUSIONS: This study highlights the important role that drug use plays in contributing to increased dependency on sex work for income among street-based FSWs in an urban Canadian setting, including a positive dose-response relationship between money spent on drugs and sex work income. These findings indicate a crucial need to scale up access and availability of evidence-based harm reduction and treatment approaches, including policy reforms, improved social support and economic choice for vulnerable women.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".