Recreational Cannabis Legislation: substance use and impaired driving among Canadian rural and urban postsecondary students
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».