A systematic review of cannabis health warning research
Bibliographic record
Abstract
Background: Cannabis legalization provides an opportunity to communicate with consumers through mandated health warnings on cannabis packaging. However, research on cannabis health warnings is a nascent field. Therefore, a review is needed to synthesize cannabis health warning research and inform ongoing policy discussions. Methods: This paper used systematic review guidelines to search online databases, including PubMed Central, Scopus, Web of Science, Jstor, Communication and Mass Media Complete, Medline, PsycINFO, and Google Scholar. Search strings combined the terms "cannabis" or "marijuana" with "health warning" or "health warning message" or "warning label" or "health warning label" or "health information label." Results were synthesized narratively. Results: = 6; 35.3 %). Evidence indicated mandated cannabis health warnings improved noticing and recall of health warning content. Cannabis health warnings describing risks of addiction were consistently rated the least effective. Pictorial cannabis health warnings generally outperformed text-only warnings when displayed on their own, while experiments with warnings on products had mixed results. Cannabis health warnings decreased product appeal, mainly when package branding was minimized. Conclusions: Health warnings on cannabis packaging are an important strategy to communicate risk to consumers. Mandating warnings increased notice, recall, and health knowledge. Warnings with pictures and describing specific risks were most effective, as was showing warnings without product branding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".