Housing Instability and Evictions Linked to Elevated Intimate Partner and Workplace Violence Among Women Sex Workers in Vancouver, Canada: Findings of a Prospective, Community-Based Cohort, 2010–2019
Bibliographic record
Abstract
Objectives. To model the relationship of unstable housing and evictions with physical and sexual violence perpetrated against women sex workers in intimate and workplace settings. Methods. We used bivariate and multivariable logistic regression with generalized estimating equations to model the association of unstable housing exposure and evictions with intimate partner violence (IPV) and workplace violence among a community-based longitudinal cohort of cisgender and transgender women sex workers in Vancouver, Canada, from 2010 through 2019. Results. Of 946 women, 85.9% experienced unstable housing, 11.1% eviction, 26.2% IPV, and 31.8% workplace violence. In multivariable generalized estimating equation models, recent exposure to unstable housing (adjusted odds ratio [AOR] = 2.04; 95% confidence interval [CI] = 1.45, 2.87) and evictions (AOR = 2.45; 95% CI = 0.99, 6.07) were associated with IPV, and exposure to unstable housing was associated with workplace violence (AOR = 1.46; 95% CI = 1.06, 2.00). Conclusions. Women sex workers face a high burden of unstable housing and evictions, which are linked to increased odds of intimate partner and workplace violence. Increased access to safe, women-centered, and nondiscriminatory housing is urgently needed. (Am J Public Health. 2023;113(4):442–452. https://doi.org/10.2105/AJPH.2022.307207 )
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".