Investigating the spatial association between supervised consumption services and homicide rates in Toronto, Canada, 2010–2023: an ecological analysis
Bibliographic record
Abstract
Background: Supervised consumption services (SCS) are effective at preventing overdose mortality. However, their effect on public safety remains contested. We investigated homicide rates in areas near SCS in Toronto. Methods: We classified coroner-reported fatal shootings and stabbings (January 1st, 2010 to September 30th 2023) by geographic zone: within 500 m ('near'), between 500 m and 3 km ('far'), and beyond 3 km of an SCS ('out'). We then used Poisson regression to calculate the rate ratio (RR) across zones 18, 36, 48, and 60 months pre vs. post SCS implementation. Finally, we compared spatial homicide incidence prior to and after the date of the implementation of each SCS using interrupted time series (ITS). Findings: Overall, 956 homicides occurred, and 590 (62%) were fatal shootings and stabbings. There was no meaningful change in the rate of fatal shootings and stabbings within 3 kms of SCS (near and far zones) after their implementation. However, between 48 and 60 months pos-implementation, we detected an increase in out zones. In an ITS analysis, we observed a reduction in the monthly incidence in near zones and an increase in out zones. Interpretation: SCS implementation was not associated with increased homicide rates; instead, we observed a reduction in monthly incidence near SCS. These results may inform drug market activity responses that optimize community health and safety. Funding: Canadian Institutes of Health Research, the New Frontiers in Research Fund, St. Michael's Hospital Foundation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".