Do Mandatory Health Warning Labels on Consumer Products Increase Recall of the Health Risks of Cannabis?
Bibliographic record
Abstract
Introduction Warning labels are an important source of health information. This study examined awareness of health warnings on cannabis packages over time in Canada—where large rotating messages are mandated—versus US states with legal adult-use cannabis, which have less comprehensive regulations.Methods Repeat cross-sectional data were collected from the International Cannabis Policy Study online surveys among past 12-month cannabis consumers in Canada and the US (n = 38,448). Free recall of warning messages was assessed in 2018–2020, followed by a prompted recognition task (2020 only). Adjusted logistic regression models tested differences in free recall and recognition of warnings between Canada and US states with and without legal adult-use cannabis (“legal” and “illegal” states, respectively).Results Free recall of ≥1 warning increased to a greater extent in Canada from 2018 (5%; pre-legalization) to 2019 (13%; post-legalization) compared to US “legal” (AOR = 1.93, p < 0.001) and “illegal” states (AOR = 1.80, p = 0.007), and from 2018 to 2020 (5% vs. 15%) compared to US “legal” states (AOR = 2.23, p = 0.027). In all jurisdictions, free recall of warnings was higher among more frequent consumers (p < 0.001) and those who purchased products from legal retail stores/websites (p < 0.001). With few exceptions, when a specific message was mandated (e.g., impaired driving), consumers were more likely to both freely recall and recognize that message (all p < 0.05).Conclusions Cannabis legalization is associated with greater recall of health warning messages. Awareness of specific warning messages was higher in jurisdictions where the associated warning was mandated on packages, suggesting that warning labels may improve knowledge of cannabis-related health risks.Supplemental data for this article is available online at
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".