Patterns of Cannabis Use Before and After Legalization in Canada
Bibliographic record
Abstract
ABSTRACT Objectives: With the legalization of nonmedicinal cannabis in 2018, it is important to understand how cannabis use has changed postlegalization. Legalization of cannabis also allows a further understanding of associations between cannabis use and sex, age, smoking, and vaping. Since cannabis is provincially regulated, provincial comparisons may help to understand the implications of various policy options. Methods: Data from the 2017 Canadian Tobacco, Alcohol, and Drugs Survey provided a prelegalization baseline for prevalence of cannabis use (n = 16,349). The 2019 Canadian Tobacco and Nicotine Survey was used as a postlegalization comparison (n = 8,614). The cannabis items had different wording, necessitating an approximation for the past 30-day prevalence in 2017. Variables of interest included sex, age, province, cigarette smoking status, and vaping. Results: The past 30-day prevalence of cannabis use increased from approximately 9% in 2017 to nearly 11% (95% confidence intervals: 10.1, 11.7) in 2019. However, due to the approximation of the 2017 frequency, it was not possible to confirm that this increase was statistically significant. Expected associations between cannabis use and sex, age, smoking, and vaping were found in both 2017 and 2019. Provinces that allow personal cultivation of cannabis had a higher frequency of use in 2019, odds ratio = 1.58 (95% confidence intervals: 1.27, 1.95). No differences in use were seen in provinces adopting different sales models. Conclusions: The prevalence of cannabis use in the Canadian population has increased from 2017 to 2019 by approximately 2% in absolute terms. Few differences were seen between provinces in 2019, despite differing regulatory approaches. Objectifs: Avec la légalisation du cannabis non médicinal en 2018, il est important de comprendre comment la consommation de cannabis a changé après la légalisation. La légalisation du cannabis permet également de mieux comprendre les rapports entre la consommation de cannabis et le sexe, l’âge, le tabagisme et le vapotage. Étant donné que le cannabis est réglementé par les provinces, les comparaisons provinciales peuvent aider à comprendre les implications de diverses options stratégiques. Méthodes: Les données de l’Enquête canadienne sur le tabac, l’alcool et les drogues (ECTAD) de 2017 ont fourni une base de référence avant la légalisation de la consommation de cannabis (N = 16 349). L’Enquête canadienne sur le tabac et la nicotine (ECTN) de 2019 a été utilisée comme outil de post-légalisation en comparaison (N = 8 614). Les questions portant sur le cannabis avaient une formulation différente, nécessitant une approximation de la prévalence au cours des 30 derniers jours en 2017. Les variables d’intérêt comprenaient le sexe, l’âge, les particularités de chaque province, le tabagisme et le vapotage. Résultats: La prévalence de la consommation de cannabis au cours des 30 derniers jours indiquait une hausse d’environ 9% en 2017 à près de 11% (IC à 95%: 10.1, 11.7) en 2019. Cependant, en raison de l’approximation de la fréquence de la consommation du cannabis en 2017, il a été impossible de confirmer statistiquement l’importance de cette augmentation. Des associations probables entre la consommation de cannabis et le sexe, l’âge, le tabagisme et le vapotage ont été trouvées dans ces statistiques de 2017 et 2019. Les provinces qui autorisent la culture personnelle du cannabis avaient une fréquence de consommation plus élevée en 2019, odds ratio = 1,58 (IC à 95%: 1.27, 1.95). Par contre, aucune différence d’utilisation n’a été observée dans les provinces adoptant des modèles de vente différents. Conclusions: La hausse de la consommation de cannabis dans la population canadienne a augmenté de 2017 à 2019 d’environ 2% en termes absolus. Peu de différences ont été observées entre les provinces en 2019, malgré des approches réglementaires différentes.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".