High Prevalence and Partner Correlates of Physical and Sexual Violence by Intimate Partners among Street and Off-Street Sex Workers
Bibliographic record
Abstract
OBJECTIVES: Intimate partner violence (IPV) is associated with increased risk of HIV among women globally. There is limited evidence and understanding about IPV and potential HIV risk pathways among sex workers (SWs). This study aims to longitudinally evaluate prevalence and correlates of IPV among street and off-street SWs over two-years follow-up. METHODS: Longitudinal data were drawn from an open prospective cohort, AESHA (An Evaluation of Sex Workers Health Access) in Metro Vancouver, Canada (2010-2012). Prevalence of physical and sexual IPV was measured using the WHO standardized IPV scale (version 9.9). Bivariate and multivariable logistic regression using Generalized Estimating Equations (GEE) were used to examine interpersonal and structural correlates of IPV over two years. RESULTS: At baseline, 387 SWs had a male, intimate sexual partner and were eligible for this analysis. One-fifth (n = 83, 21.5%) experienced recent physical/sexual IPV at baseline and 26.2% over two-years follow-up. In multivariable GEE analysis, factors independently correlated with physical/sexual IPV in the last six months include: childhood (<18 years) sexual/physical abuse (adjusted odds ratio [AOR] = 2.05, 95% confidence interval [CI]: 1.14-3.69), inconsistent condom use for vaginal and/or anal sex with intimate partner (AOR = 1.84, 95% CI: 1.07-3.16),
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".