Toronto’s Supervised Consumption Sites and Local Crime
Bibliographic record
Abstract
Importance: Beginning in August 2017, 9 overdose prevention sites and supervised consumption sites (OPS/SCS) began operating in Toronto, Canada. Following years of community pushback stating that these sites increased local crime and disorder, they were closed in March 2025. Objective: To examine the association between OPS/SCS and local crime and disorder. Design, Setting, and Participants: This ecological cohort study used Toronto Police Service data to compare crime incidence before and after OPS/SCS implementation using an interrupted time series study design. Analysis was restricted to crimes that occurred within city boundaries between January 1, 2014, and June 30, 2025. Main Outcomes and Measures: The study used monthly event counts of all assaults, auto thefts, break and enters, robberies, thefts over $5000, bicycle thefts, and thefts from motor vehicles as the 7 outcomes. Incidence within 400 m of the geolocation of each OPS/SCS before and after implementation were compared and results were pooled for population-level estimates. Results: Within 400 m (approximately a quarter mile), OPS/SCS implementation was associated with increases in break and enters (49.88%; 95% CI, 27.03% to 76.84%), and to a lesser extent, thefts from motor vehicles (20.03%; 95% CI, -0.63% to 44.99%). However, monthly trends for break and enters (-1.19%; 95% CI, -1.71% to -0.68%), robberies (-1.32%; 95% CI, -1.93% to -0.70%), thefts over $5000 (-1.48%; 95% CI, -2.45% to -0.50%), bicycle thefts (-1.82%; 95% CI, -2.93% to -0.68%), and thefts from motor vehicles (-1.30%; 95% CI, -2.18% to -0.42%) declined. Site-specific results revealed some OPS/SCS were associated with increases in crime while most were not. Conclusions and Relevance: This ecological cohort study found that the association between Toronto's OPS/SCS and crime was generally neutral to beneficial. OPS/SCS were associated with increases in break and enters, and to a lesser extent thefts from motor vehicles, immediately postimplementation. Incidents of crimes declined with time.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".