Effects of cannabis legalisation on patterns of cannabis consumption among adolescents in <scp>Ontario, Canada</scp> (2001–2019)
Bibliographic record
Abstract
INTRODUCTION: Cannabis legalisation was enacted on 17 October 2018 in Canada. Accordingly, the effects of cannabis legalisation on patterns of cannabis consumption were examined among adolescents, including on cannabis initiation, any cannabis use, daily cannabis use and cannabis dependence. METHODS: Data from a biennial population-based, cross-sectional survey of students in Ontario were pooled in a pre-post design (2001-2019; N = 89,238). Participants provided self-reports of cannabis initiation, any cannabis use, daily cannabis use and cannabis dependence. Long-term trends in these patterns of cannabis consumption over two decades of observation were characterised to provide a broader context of usage. The effects of cannabis legalisation on patterns of cannabis consumption were quantified using logistic regression analyses. RESULTS: Long-term trends over the two decades of observation indicated that cannabis initiation decreased and then increased (p = 0.0220), any cannabis use decreased and daily cannabis use decreased (p < 0.0001 and p = 0.0001, respectively) and cannabis dependence remained unchanged (p = 0.1187). However, in comparisons between the pre-cannabis legalisation period (2001-2017) and the post-cannabis legalisation period (2019), cannabis legalisation was not associated with cannabis initiation (odds ratio; 95% confidence interval 1.00; 0.79-1.27), but it was associated with an increased likelihood of any cannabis use (1.31; 1.12-1.53), daily cannabis use (1.40; 1.09-1.80) and cannabis dependence (1.98; 1.29-3.04). DISCUSSION AND CONCLUSIONS: Cannabis legalisation was not associated with cannabis initiation, but it was associated with an increased likelihood of any cannabis use, daily cannabis use and cannabis dependence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".