Cannabis legalization and cannabis use, daily cannabis use and cannabis-related problems among adults in Ontario, Canada (2001–2019)
Bibliographic record
Abstract
BACKGROUND: In the context of cannabis legalization in Canada, we examined the effects on cannabis patterns of consumption, including cannabis use, daily cannabis use and cannabis-related problems. In addition, we examined differential effects of cannabis legalization by age and sex. METHODS: A pre-post design was operationalized by combining 19 iterations of the Centre for Addiction and Mental Health (CAMH) Monitor Surveys (N = 52,260; 2001-2019): repeated, population-based, cross-sectional surveys of adults in Ontario. Participants provided self-reports of cannabis use (past 12 months), daily cannabis use (past 12 months) and cannabis-related problems though telephone interviews. The effects of cannabis legalization on cannabis patterns of consumption were examined using logistic regression analyses, with testing of two-way interactions to determine differential effects by age and sex. RESULTS: Cannabis use prevalence increased from 11 % to 26 % (p < 0.0001), daily cannabis use prevalence increased from 1 % to 6 % (p < 0.0001) and cannabis-related problems prevalence increased from 6 % to 14 % (p < 0.0001) between 2001 and 2019. Cannabis legalization was associated with an increased likelihood of cannabis use (OR, 95 % CI: 1.62, 1.40-1.86), daily cannabis use (1.59, 1.21-2.07) and cannabis-related problems (1.53, 1.20-1.95). For cannabis-related problems, a significant two-way interaction was observed between cannabis legalization and age (p = 0.0001), suggesting differential effects among adults ≥55 years. CONCLUSIONS: Cannabis legalization was associated with an increased likelihood of cannabis use, daily cannabis use and cannabis-related problems. Given increases in these cannabis patterns of consumption, broader dissemination and uptake of targeted prevention tools is indicated.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".