Supervised consumption sites and population-level overdose mortality: a systematic review of recent evidence, 2016–2024
Bibliographic record
Abstract
INTRODUCTION: The overdose crisis is one of the most serious public health challenges in North America. Supervised consumption sites (SCSs) effectively prevent onsite overdose deaths and connect people to health services, but their association with populationlevel overdose mortality remains unclear. METHODS: We searched Embase, Global Health and MEDLINE databases for studies examining associations between SCSs and population-level overdose mortality during the post-2016 overdose crisis (January 2016 to November 2024). Two reviewers, working independently, screened studies, extracted data and assessed study quality using standardized tools (PROSPERO CRD42023406080). RESULTS: Six studies, all from Canada, met the inclusion criteria. In the four quasiexperimental studies, two large-scale analyses of local health areas or public health units found no significant associations between SCS measures and overdose mortality within provinces. Some analyses of smaller urban areas showed protective associations, although this finding was not consistent across studies. Two observational studies suggested associations between SCS and lower mortality rates, though with methodological limitations. CONCLUSION: Province-wide analyses generally did not detect significant associations between areas with and without SCSs and population-level overdose mortality. Analyses suggest that SCSs in some smaller urban contexts were associated with less overdose mortality, though findings were inconsistent. Further research is needed to understand how geographic scale, implementation context and limited service coverage may influence the detection and magnitude of potential effects of SCSs on overdose mortality.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".