Cannabis and driving: A repeat cross-sectional analysis of driving after cannabis use pre- vs. post-legalization of recreational cannabis in Canada
Bibliographic record
Abstract
OBJECTIVE: The potential impact of cannabis legalization on driving after cannabis use is an important public health consideration. The current paper examined the prevalence of driving after cannabis use and being a passenger of a driver who recently consumed cannabis pre- and five years post- legalization of recreational cannabis. METHOD: National population-based surveys were conducted annually between 2018 and 2023 as part of the International Cannabis Policy Study (ICPS). A total of 93,933 participants aged 16-65 years from Canada were included in the analysis. Logistic regression models assessed trends in driving after cannabis use by age, sex-at-birth, income adequacy, ethnicity, and educational attainment. RESULTS: In 2018, 5.7 % of all respondents and 19.9 % of past 12-month consumers reported driving within 2 h of cannabis consumption in the past year. Driving after consumption increased moderately in the five years post legalization among all participants, with a significantly higher prevalence reported in 2022 (8.8 % vs. 5.7 %, OR = 1.43, 95 % CI = 1.22, 1.66, p < 0.001) and 2023 (7.6 % vs. 5.7 %, OR = 1.20, 95 % CI = 1.03, 1.40, p = 0.018) than in 2018. However, driving after consumption remained stable among past 12-month consumers, with a moderately lower prevalence in 2023 than in 2018 (18.3 % vs. 19.9 %, OR = 0.81, 95 % CI = 0.68, 0.97, p = 0.024). CONCLUSIONS: The increase in the overall rate of driving after use likely reflects the increase in consumption among all Canadians following recreational cannabis legalization. There was no evidence to support changes in the overall prevalence of passenger behaviour following legalization. Differences across sociodemographic variables are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".