Community mapping of sex work criminalization and violence: impacts on HIV treatment interruptions among marginalized women living with HIV in Vancouver, Canada
Bibliographic record
Abstract
Despite the high HIV burden faced by sex workers, data on access and retention in antiretroviral therapy (ART) are limited. Using an innovative spatial epidemiological approach, we explored how the social geography of sex work criminalization and violence impacts HIV treatment interruptions among sex workers living with HIV in Vancouver over a 3.5-year period. Drawing upon data from a community-based cohort (AESHA, 2010-2013) and linked external administrative data on ART dispensation, GIS mapping and multivariable logistic regression with generalized estimating equations to prospectively examine the effects of spatial criminalization and violence near women's places of residence on 2-day ART interruptions. Analyses were restricted to 66 ART-exposed women who contributed 208 observations and 83 ART interruption events. In adjusted multivariable models, heightened density of displacement due to policing independently correlated with HIV treatment interruptions (AOR: 1.02, 95%CI: 1.00-1.04); density of legal restrictions (AOR: 1.30, 95%CI: 0.97-1.76) and a combined measure of criminalization/violence (AOR: 1.00, 95%CI: 1.00-1.01) were marginally correlated. The social geography of sex work criminalization may undermine access to essential medicines, including HIV treatment. Interventions to promote 'enabling environments' (e.g. peer-led models, safer living/working spaces) should be explored, alongside policy reforms to ensure uninterrupted treatment access.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".