Trends in youth cannabis use across cannabis legalization: Data from the COMPASS prospective cohort study
Bibliographic record
Abstract
Canada legalized recreational cannabis use for adults on October 17, 2018 with decision-makers emphasising the need to reduce cannabis use among youth. We sought to characterise trends of youth cannabis use before and after cannabis legalization by relying on a quasi-experimental design evaluating cannabis use among high school students in Alberta, British Columbia, Ontario, and Québec who participated in the COMPASS prospective cohort study. Overall trends in use were examined using a large repeat cross-sectional sample (n = 102,685) at two time points before legalization (16/17 and 17/18 school years) and one after (18/19 school year). Further differential changes in use among students affected by legalization were examined using three sequential four-year longitudinal cohorts (n = 5,400) of students as they progressed through high school. Youth cannabis use remains common with ever-use increasing from 30.5% in 2016/17 to 32.4% in 2018/19. In the repeat cross-sectional sample, the odds of ever use in the year following legalization were 1.05 times those of the preceding year (p = 0.0090). In the longitudinal sample, no significant differences in trends of cannabis use over time were found between cohorts for any of the three use frequency metrics. Therefore, it appears that cannabis legalization has not yet been followed by pronounced changes on youth cannabis use. High prevalence of youth cannabis use in this sample remains a concern. These data suggest that the Cannabis Act has not yet led to the reduction in youth cannabis use envisioned in its public health approach.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".