Perceptions of cannabis use risk to mental health among youth in Canada, England and the United States from 2017 to 2021
Bibliographic record
Abstract
BACKGROUND: There is little research examining perceptions of cannabis use risk to mental health in countries with differing cannabis regulations. This study therefore examines such perceptions among youth between 2017 and 2021 in Canada (non-medical cannabis legalized in October 2018), England (highly-restricted medical cannabis legalized November 2018), and the US (non-medical cannabis legal in some states). METHODS: Seven repeat cross-sectional online surveys were conducted between July 2017 to August 2021 among youth aged 16-19 in Canada (N=29,420), England (N=28,155), and the US (N=32,974). Logistic regression models, stratified by country, were used to examine perceptions of cannabis use risk to mental health over time, adjusting for age group, sex, race/ethnicity, cannabis use and, for the US only, state-level cannabis legalization. RESULTS: Perceptions that cannabis use posed "no risk" to mental health decreased between July 2017 and August 2021 in Canada (6.1-4.4%; AOR=0.64, 95% CI=0.52-0.78) and the US (14.0-11.3%; AOR=0.74, 0.65-0.84) but not England (3.7-4.5%; AOR=1.21, 0.97-1.52). No significant changes were observed from immediately before (August 2018) to after (August 2019) legalization of non-medical cannabis in Canada (AOR=0.99, 0.83-1.20) or highly-restricted medical cannabis in England (AOR=0.90, 0.70-1.17). In the US, perceptions of "no risk" were more likely in states where cannabis use was illegal (15.0%) compared with legal non-medical (12.2%) (AOR=0.68, 0.63-0.74). CONCLUSION: There were modest decreases in perceptions that cannabis use poses no risk to mental health in Canada and the US between 2017 and 2021 but no clear association with cannabis legalization status.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".