A quantitative bias analysis of the association between supervised consumption service use and sharing of injection equipment among people who inject drugs in Toronto, Canada
Bibliographic record
Abstract
PURPOSE: Supervised consumption services (SCS) prevent fatal overdoses, but less is known about their contemporary impact on infectious disease risk. METHODS: We used quantitative bias analyses (QBA) to evaluate the association between SCS use and sharing of injection equipment while accounting for measurement error, selection bias, and unmeasured confounding, drawing on a cross-sectional sample of 695 people who inject drugs in Toronto, Canada surveyed from 2018 to 2020. We estimated the association between self-reported SCS use and equipment sharing using modified Poisson regression. Sensitivity analyses varied SCS use categories and definitions of sharing. QBA estimated the impact of misclassification, selection bias, and unmeasured confounding. RESULTS: Almost all participants (96.6 %) had recently used a needle and syringe program. Frequent SCS use (≥26 % of injections) was not associated with sharing equipment (adjusted prevalence ratio (aPR): 0.98; 95 % CI: 0.77-1.24). Results of sensitivity analyses did not meaningfully differ. In multiple bias analysis, the median bias-adjusted PR of 1.04 (range: 0.83-1.42) suggested no association between regular SCS use (≥75 % of injections) and syringe sharing. CONCLUSIONS: In summary, SCS use was not associated with equipment sharing in a context of high needle and syringe program coverage. Misclassification, selection bias, and unmeasured confounding did not appear to impact the observed associations.
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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 | 0.046 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".