Perceptions of the health risks of cannabis: estimates from national surveys in Canada and the United States, 2018–2019
Bibliographic record
Abstract
Few studies have compared knowledge of the specific health risks of cannabis across jurisdictions. This study aimed to examine perceptions of the health risks of cannabis in Canada and US states with and without legal non-medical cannabis. Cross-sectional data were collected from the 2018 and 2019 International Cannabis Policy Study online surveys. Respondents aged 16-65 (n = 72 459) were recruited from Nielsen panels using non-probability methods. Respondents completed questions on nine health effects of cannabis (including two 'false' control items). Socio-demographic data were collected. Regression models tested differences in outcomes between jurisdictions and by frequency of cannabis use, adjusting for socio-demographic factors. Across jurisdictions, agreement with statements on the health risks of cannabis was highest for questions on driving after cannabis use (66-80%), use during pregnancy/breastfeeding (61-71%) and addiction (51-62%) and lowest for risk of psychosis and schizophrenia (23-37%). Additionally, 12-18% and 6-7% of respondents agreed with the 'false' assertions that cannabis could cure/prevent cancer and cause diabetes, respectively. Health knowledge was highest among Canadian respondents, followed by US states that had legalized non-medical cannabis and lowest in states that had not legalized non-medical cannabis (P < 0.001). Overall, the findings demonstrate a substantial deficit in knowledge of the health risks of cannabis, particularly among frequent consumers.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".