Changes in patterns of use and perceptions of cannabis among students in Canada: A decade of data from the Canadian Student Alcohol and Drugs Survey
Bibliographic record
Abstract
Introduction: The Canadian Student Alcohol and Drugs Survey is a biennial, repeat cross-sectional survey of grade 7-12 students in the Canadian provinces. This study examined cannabis-related behaviours at five timepoints before and after legalization of cannabis for non-medical purposes. Methods: Trends over time were examined using data from 2014-15 to 2023-24 (n = 264,558). Binary logistic regression examined changes in cannabis use and related behaviours, including frequency; usual method of use; perceived risk and access; usual source; and motor vehicle behaviours. Data were stratified by sex and grade group (grade 7-9 vs. 10-12). Results: Overall, there was no change in prevalence of past 12-month, past 30-day, or frequent cannabis use (p > 0.05 for all); however, modest increases were observed among females and grade 7-9 students (p < 0.05 for both). Vaping surpassed smoking as the most common method of consumption in 2023-24. Smoking and dabbing cannabis decreased over time, whereas vaping and eating cannabis increased (p < 0.001 for all). Perceived risk of regularly smoking cannabis decreased (p < 0.001), and perceived ease of cannabis access increased (p < 0.001). The most common cannabis sources were social sources. There was no change in driving after using cannabis (p > 0.05), whereas there was a recent increase in riding with a driver who had used cannabis (p < 0.001). Conclusion: While legalization and regulation of cannabis for non-medical purposes was not associated with increases in overall cannabis use among students in Canada, increasing rates of use in females and younger students, and changes in perceptions of risk and accessibility require continued monitoring.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| 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".