Recreational Cannabis Legislation: substance use and impaired driving among Canadian rural and urban postsecondary students
Bibliographic record
Abstract
BACKGROUND: Investigation of cannabis use trends among emerging adults (EA, aged between 18 and 24 years) following 2018 Canadian Recreational Cannabis Legislation (RCL) is critical. EAs report the heaviest cannabis use in Canada and are particularly vulnerable to the onset of problematic substance use. OBJECTIVES: To describe and compare post-RCL use of cannabis and other state-altering substances, as well as the prevalence of impaired driving, among EA postsecondary students in both rural and urban settings, studying on one of five campuses in either Manitoba, Ontario, or Quebec. METHODS: For this quantitative cross-sectional study, a self-report survey was administered to 1496 EA postsecondary students in the months following RCL (2018-2019). Multiple logistic regression analyses were conducted to explore the influence of provincial and urban/rural living contexts on recreational cannabis use, other state-altering substance use and impaired driving behaviours, adjusting for sociodemographic variables. RESULTS: Statistically significant differences were observed between cohorts in almost all measures. Quebec students were more likely to have consumed cannabis during their lifetime (AOR = 1.41, 95% CI [1.05, 1.90]) than all other cohorts. Rural cohorts all had greater odds of reporting consumption of cannabis during the previous year compared to urban cohorts (AOR = 1.32, 95% CI [1.04, 1.67]). However, the relation between cannabis use in the last month and operating a motor vehicle after using cannabis (lifetime and past month) and living context differed between subjects in Quebec and those in the two other provinces. Quebec's students having lived mostly in urban contexts had greater odds of using cannabis in the past month and operating a motor vehicle after using cannabis (lifetime and past month) than those in rural contexts; the opposite was observed in Manitoba and Ontario. Differing interprovincial prohibitive/permissive legislation and licit cannabis infrastructure appeared to have little impact on post-RCL substance use. CONCLUSIONS: In Manitoba and in Ontario, rural/urban living context seems to better predict substance use and related road-safety practices, suggesting these trends supersede permissive/prohibitive provincial legislation and licit cannabis-related infrastructures. Further investigation into sociodemographic factors influencing state-altering substance use and impaired driving, and maintaining tailored cannabis misuse prevention campaigns, is warranted on Canadian campuses.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".