Survival sex work involvement among street-involved youth who use drugs in a Canadian setting
Bibliographic record
Abstract
BACKGROUND: Drug users engaged in survival sex work are at heightened risk for drug- and sexual-related harms. We examined factors associated with survival sex work among street-involved youth in Vancouver, Canada. METHODS: From September 2005 to November 2007, baseline data were collected for the At-Risk Youth Study (ARYS), a prospective cohort of street-recruited youth aged 14-26 who use illicit drugs. Using multiple logistic regression, we compared youth who reported exchanging sex for money, drugs etc. with those who did not. RESULTS: The sample included 560 youth: median age 22; 179 (32%) female; 63 (11%) reporting recent survival sex work. Factors associated with survival sex work in multivariate analyses included non-injection crack use [adjusted odds ratio (AOR) = 3.45, 95% confidence interval (CI): 1.75-6.78], female gender (AOR = 3.02, 95% CI: 1.66-5.46), Aboriginal ethnicity (AOR = 2.35, 95% CI: 1.28-4.29) and crystal methamphetamine use (AOR = 2.02, 95% CI: 1.13-3.62). In subanalyses, the co-use of crack cocaine and methamphetamine was shown to be driving the association between methamphetamine and survival sex work. CONCLUSIONS: This study demonstrates a positive interactive effect of dual stimulant use in elevating the odds of survival sex work among street youth who use drugs. Novel approaches to reduce the harms associated with survival sex work among street youth who use stimulants are needed.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 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".