Violence, policing, and systemic racism as structural barriers to substance use treatment amongst women sex workers who use drugs: Findings of a community-based cohort in Vancouver, Canada (2010–2019)
Bibliographic record
Abstract
BACKGROUND: Despite a high prevalence of substance use among women sex workers (SWs), rigorous social epidemiologic data on substance use treatment experiences among SWs remains limited. Given these gaps and the disproportionate burden of criminalization borne by Indigenous SWs, we evaluated (1) structural correlates of unsuccessful attempts to access substance use treatment; and (2) the interaction between policing and Indigenous ancestry on unsuccessful attempts to access treatment among SWs who use drugs. METHODS: Prospective data were from an open community-based cohort of women SWs (2010-2019) in Vancouver, Canada. Bivariate and multivariable logistic regression with generalized estimating equations(GEE) assessed correlates of unsuccessful attempts to access treatment. A multivariable GEE confounder model examined the interaction between Indigenous ancestry and policing on unsuccessful attempts to access treatment. RESULTS: Amongst 645 SWs who used drugs, 32.1 % reported unsuccessful attempts to access substance use treatment during the 9.5-year study. In multivariable GEE analysis, unsuccessful substance use treatment access was associated with identifying as a sexual/gender minority (AOR: 1.90, 95 %CI:1.37-2.63), opioid use (AOR: 1.43, 95 %CI: 1.07-1.91), and exposure to homelessness (AOR: 1.72; 95 %CI:1.33-2.21), police harassment (AOR: 1.48, 95 %CI:1.03-2.13), workplace violence (AOR: 1.80, 95 %CI: 1.31-2.49) and intimate partner violence (AOR: 2.11, 95 %CI:1.50-2.97). In interaction analysis, Indigenous SWs who experienced police harassment faced the highest odds of unsuccessful attempts to access substance use treatment (AOR: 2.59, 95 %CI:1.65-4.05). CONCLUSION: Findings suggest a need to scale-up culturally-safe, trauma-informed addictions, gender-based violence, and sex worker services, alongside dismantling of systemic racism across and beyond health and addictions services.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".