Did the cannabis recreational use law affect traffic crash outcomes in Toronto? Building evidence for the adequate number of authorised cannabis stores' thresholds
Bibliographic record
Abstract
INTRODUCTION: In the past decade, a group of studies has begun to explore the association between cannabis recreational use policies and traffic crashes. After these policies are set in place, several factors may affect cannabis consumption, including the number of cannabis stores (NCS) per capita. This study examines the association between the enactment of Canada's Cannabis Act (CCA) (18 October 2018) and the NCS (allowed to function from 1 April 2019) with traffic injuries in Toronto. METHODS: We explored the association of the CCA and the NCS with traffic crashes. We applied two methods: hybrid difference-in-difference (DID) and hybrid-fuzzy DID. We used generalised linear models using CCA and the NCS per capita as the main variables of interest. We adjusted for precipitation, temperature and snow. Information is gathered from Toronto Police Service, Alcohol and Gaming Commission of Ontario, and Environment Canada. The period of analysis was from 1 January 2016 to 31 December 2019. RESULTS: Regardless of the outcome, neither the CCA nor the NCS is associated with concomitant changes in the outcomes. In hybrid DID models, the CCA is associated with non-significant decreases of 9% (incidence rate ratio 0.91, 95% confidence interval 0.74,1.11) in traffic crashes and in the hybrid-fuzzy DID models, the NCS are associated with nonsignificant decreases of 3% (95% confidence interval - 9%, 4%) in the same outcome. DISCUSSION AND CONCLUSIONS: This study observes that more research is needed to better understand the short-term effects (April to December 2019) of NCS in Toronto on road safety outcomes.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".