Adverse outcomes of cannabis use in Canada, before and after legalisation of non-medical cannabis: cross-sectional analysis of the International Cannabis Policy Study
Bibliographic record
Abstract
OBJECTIVES: To date, there is little evidence on the extent to which cannabis legalisation affects the prevalence of adverse events from cannabis at the population level. The current study examined trends in the prevalence of adverse events among people who consumed cannabis before and after Canada's legalisation of recreational cannabis. DESIGN: Data come from the first four survey waves of the International Cannabis Policy Study, which were conducted online annually immediately prior to non-medical cannabis legalisation in Canada in October 2018, and in the three following years (2019-2021). PARTICIPANTS: The current analysis included 18 285 Canadian respondents aged 16-65 who reported cannabis use in the past 12 months. OUTCOME MEASURES: Primary outcomes included types of adverse events experienced from cannabis use, medical help-seeking and the types of products used. Weighted logistic regression models examined differences in help-seeking, emergency room usage and the experience of cannabinoid hyperemesis syndrome across survey years. RESULTS: Approximately one-third of people who consume cannabis reported experiencing at least one adverse event within the past 12 months, including 5% of consumers who sought medical help for an adverse event, most commonly for panic attacks, feeling faint/dizzy/passing out, heart/blood pressure problems and nausea/vomiting. The prevalence of seeking help and the types of adverse events were similar before (2018) and after legalisation (2019-2021); however, the proportion of consumers seeking help from emergency rooms increased postlegalisation (F=2.77, p=0.041). Adverse events were associated with various product types, with dried flower and oral oils accounting for the largest proportion of events. Help-seeking associated with cannabis edibles significantly decreased after legalisation (p=0.001). CONCLUSIONS: Substantial proportions of people who consume cannabis report adverse events, suggesting widespread difficulty in 'dosing'. Few changes were observed in the prevalence of adverse events reported by consumers since legalisation; however, the location of medical help-seeking and associated products used have changed postlegalisation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".