The protective association of social cohesion on sex workers’ experiences of violence and access to community support: Impacts of resource sharing, trust and connection among a community-based cohort in Metro Vancouver, Canada (2010–2022)
Bibliographic record
Abstract
OBJECTIVES: To measure recent social cohesion (resource sharing, trust and support) and its association with (1) sexual/physical violence, and (2) engagement with sex work-specific services among women sex workers in Metro Vancouver, Canada. METHODS: Prospective data (January 2010-August 2022) were drawn from an open cohort of 900+ women sex workers. We developed multivariable logistic regression confounder models with generalized estimating equations (GEE) to examine associations between social cohesion and recent (1) physical/sexual violence and (2) engagement with sex work-specific services. RESULTS: Of 918 participants, 36.8% were Indigenous and 32.1% were Black/Women of Colour. At baseline, the median social cohesion score was 19 (IQR 16-22), out of 36, with higher levels among participants who work with other sex workers. In separate multivariable confounder models with GEE, social cohesion was associated with lower odds of recent physical/sexual violence (Adjusted Odds Ratio 0.98 per point on scale, 95% Confidence Interval 0.97, 0.99) and recent use of sex work-specific services, although only statistically significant for physical/sexual violence. CONCLUSIONS: Findings support the need to eliminate policing of work environments that promote sex workers' social cohesion and physical safety through full decriminalization.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".