Examining the impact of legalization on the prevalence of driving after using cannabis: A comparison of rural and non-rural parts of Canada
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine the likelihood of driving after using cannabis, and of being a passenger with someone who is driving after using cannabis, in rural areas and non-rural areas before and after legalization. METHODS: A multi-wave analysis of Canada's National Cannabis Survey was conducted using logistic regression with interactions to predict the prevalence of driving after using cannabis, and of being a passenger with someone who is driving after using cannabis, in relation to place of residence (rural or non-rural) and in the weeks and months before and after legalization. Three time points were compared: pre-legalization, two months following legalization and 1 year after legalization. RESULTS: At the national level, there are no significant differences between the predicted estimates of driving after using cannabis for those who live in rural and non-rural areas. However, when examining the impact of legalization, we found a significant increase in driving after using cannabis among rural residents directly following legalization. Furthermore, it was observed that this increase in driving after using cannabis returns to pre-legalization rates one year after legalization. By contrast, in the weeks and months following legalization, driving after using cannabis decreased among those living in non-rural areas, and slowly increased soon thereafter. No significant differences were observed, in either time period or group, in the prevalence of being a passenger with someone who is driving after using cannabis. CONCLUSIONS: The finding of significantly higher risk of driving after use of cannabis soon after legalization in rural areas suggests a need for more attention to address immediate concerns for public safety. The increased potential for traffic injuries and deaths in other jurisdictions contemplating legalization supports the call for more and better targeted prevention efforts in rural communities that have far too often been overlooked and under-served.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".