Community-administered naloxone for overdose reversal: The role of sex worker-led programming and occupational violence in a community-based cohort (2018–2024)
Bibliographic record
Abstract
BACKGROUND: Amidst the ongoing toxic drug crisis, sex workers who use drugs face high overdose risk alongside structural barriers to harm reduction services. Previous work has noted that occupational violence and peer-led programs can influence health outcomes for sex workers. Given the potential for community-administered take-home naloxone (THN) to reduce overdose-related harm, we evaluated longitudinal trends and uptake of THN administration, and the associations between exposure to sex work-specific programs and occupational violence and harassment with THN administration over 5.5-years (2018-2024). METHODS: Data was derived from An Evaluation of Sex Workers' Health Access, a prospective, community-based cohort of sex workers in Vancouver, Canada from September 2018-March 2024. We plotted semi-annual trends of THN administration (Aim 1) and used bivariate logistic regression with generalized estimating equations (GEE) to characterize uptake of THN administration (Aim 2). Lastly, we employed bivariate and multivariable GEE to evaluate the association between exposure to sex-work specific programs and occupational violence and harassment with THN administration (Aim 3). RESULTS: Among 427 participants, 57.9 % (N = 247) reported administering THN at least once. Exposure to sex work-specific programs was strongly associated with THN administration (Adjusted Odds Ratio [AOR] 1.26, 95 % Confidence Interval [CI] 1.02-1.55), as was exposure to violence from clients (AOR 1.72, 95 %CI 1.22-2.41) and community (AOR 1.76, 95 %CI 1.25-2.46), and police harassment (AOR 1.54, 95 %CI 1.07-2.21). CONCLUSION: Over 5.5 years, over half of participants administered THN, which was associated with exposure to sex work-specific programs and occupational violence. Violence from clients, community members, and police independently increased the likelihood of THN administration. Findings suggest that marginalized sex workers, particularly those who use drugs in occupational settings, are uniquely positioned to respond to overdoses. This supports the feasibility of implementing sex worker-specific overdose interventions and highlights the need to expand community-based, sex worker-led safety, violence prevention, and care interventions to strengthen overdose prevention efforts and improve naloxone cascade metrics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".