Police-Reported Impaired Driving After Recreational Cannabis Legalization in Canada
Bibliographic record
Abstract
INTRODUCTION: When Canada legalized recreational cannabis in 2018, it also enhanced enforcement against impaired driving. This observational study analyzed how police-reported impaired driving rates evolved after those 2 policy changes. METHODS: The study analyzed province-level annual counts of driving impaired by alcohol, drugs (including but not limited to cannabis), or both during 2009-2023. The data were published in 2024 and analyzed in 2025. Interrupted time-series regressions tested for changes in annual impairment rates per million population aged ≥16 years after 2018. Further regressions tested whether the changes were associated with legal cannabis sales, cannabis use prevalence, police drug recognition expert employment, or COVID-19 pandemic restrictions. RESULTS: During 2009-2018, alcohol-related impaired driving rates were decreasing, whereas those involving drugs were increasing. During 2019-2023, police reported 65 (95% CI=36, 93) extra drug-impaired incidents per million population annually or 42% more than the 2009-2018 trend had projected. Police also reported 280 (95% CI=134, 425) extra alcohol-impaired incidents per million population annually or 17% more than projected. New offenses covering mixed alcohol and drug impairment or unspecified-substance impairment added more incidents. Drug-impaired incidents were positively associated with drug expert employment, pandemic restrictions, and licensed cannabis sales (p<0.05). Alcohol-impaired incidents were positively associated with drug expert employment but negatively with pandemic restrictions (p<0.05). CONCLUSIONS: Canada's police-reported impaired driving rates increased after 2018 for alcohol and more so for drugs. The changes seemed associated more with enhanced enforcement and pandemic disruptions rather than with legal cannabis sales or overall cannabis use.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".