Noticing of cannabis health warning labels in Canada and the US
Bibliographic record
Abstract
INTRODUCTION: Product labelling and health warnings are important components of regulatory frameworks for consumer products such as tobacco, alcohol and food. However, evidence in the cannabis domain is limited. This study aimed to examine the reach of mandated health warnings on cannabis products using a natural experimental design. METHODS: Data are from the online International Cannabis Policy Study 2018 and 2019 surveys. Respondents were men and women aged 16 to 65 years in Canada and US states with illegal and legal nonmedical cannabis ("illegal" and "legal" states, respectively) (n = 72 549). Regression models tested differences in noticing health warnings on cannabis packages pre- and post-legalization in Canada, with comparisons to US states, adjusting for cannabis use, cannabis source and sociodemographics. RESULTS: Respondents in Canada showed a greater increase in noticing warnings (+8.9%) in 2019 (14.7%) versus 2018 (5.8%) than respondents in US "illegal" states (+2.8%) and "legal" states (+3.2%). In 2019, consumers residing in jurisdictions with legal recreational cannabis who purchased from legal retail sources were more likely to report noticing warnings than consumers who obtained cannabis from illegal/unstated sources (Canada: 40.4% vs. 15.3%; US "legal" states: 35.3% vs. 17.0%). Regular cannabis consumers were more likely to notice warnings than less frequent consumers. CONCLUSION: Mandating warning labels on cannabis products may increase exposure to messages communicating the health risks of cannabis, especially among frequent consumers and those who access the legal market.
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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 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".