Negative changes in illicit drug supply during COVID-19: Associations with use of overdose prevention and health services among women sex workers who use drugs (2020–2021)
Bibliographic record
Abstract
BACKGROUND: Women sex workers are a highly criminalized population who are over-represented amongst people who use drugs (PWUD) and face gaps in overdose prevention and harm reduction services. British Columbia, Canada continues to face a pronounced drug poisoning crisis of the illicit drug supply, which has intensified during the COVID-19 pandemic. Our objective was to examine the prevalence and structural correlates of experiencing negative changes in illicit drug supply (e.g., availability, quality, cost, or access to drugs) amongst women sex workers who use drugs during the first year of the COVID-19 pandemic. METHODS: Cross-sectional questionnaire data were drawn from a prospective, community-based cohort of women sex workers in Vancouver (AESHA) from April 2020 to 2021. Bivariate and multivariable logistic regression was used to investigate structural correlates of negative changes in drug supply during COVID-19 among sex workers who use drugs. RESULTS: Among 179 sex workers who use drugs, 68.2% reported experiencing negative changes to drug supply during COVID-19, 54.2% recently accessed overdose prevention sites, and 44.7% reported experiencing recent healthcare barriers. In multivariable analysis adjusted for injection drug use, women who reported negative changes in illicit drug supply had higher odds of experiencing recent healthcare barriers (AOR 2.28, 95%CI 1.12-4.62); those recently accessing overdose prevention sites (AOR 1.75, 95%CI 0.86-3.54) faced marginally higher odds also. CONCLUSIONS: Over two-thirds of participants experienced negative changes to illicit drug supply during the first year of the COVID-19 pandemic. The association between experiencing negative changes in the illicit drug supply and accessing overdose prevention services highlights the agency of women in taking measures to address overdose-related risks. Highly criminalized women who experience structural barriers to direct services are also vulnerable to fluctuations in the illicit drug supply. Attenuating health consequences requires interventions tailored to sex workers' needs.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".