Perceptions of plain packaging and health warning labels for cannabis among young adults: findings from an experimental study
Bibliographic record
Abstract
BACKGROUND: There is strong evidence that plain cigarette packaging and health warning labels (HWLs) reduce brand appeal and increase health knowledge. There is limited evidence examining this population-level public health approach for cannabis packaging. This issue is of particular importance in light of the recent legalization of recreational cannabis in Canada. The current study examined perceptions of plain packaging and HWLs for cannabis packages among young adults. METHODS: An online experimental study was conducted with a sample of university students in Alberta, Canada (n = 656). Respondents were randomly assigned to view cannabis packages in one of four conditions: Condition 1: branded pack, Condition 2: plain pack (uniform color, brand imagery removed, standardized font), Condition 3: branded pack with a HWL, and Condition 4: plain pack with a HWL. Respondents in Conditions 3 and 4 viewed five text-based HWLs, each corresponding to a health effect associated with cannabis use: (1) brain development, (2) mental health issues, (3) impaired driving, (4) nonlethal overdose, and (5) addiction. After viewing packs, respondents rated packs and health warnings on various measures. RESULTS: Branded packages without HWLs were rated as most appealing compared to all other packs (p < 0.001 for all contrasts). No differences were found in ratings of appeal when comparing branded and plain packs with HWLs. Warning messages for cognitive development and impaired driving were rated highest on levels of perceived effectiveness, believability, and fear, whereas the addiction warning was rated among the lowest. In general, there were gaps in health knowledge related to cannabis use, however after viewing packs with warnings (compared to viewing packs without warnings) levels of health knowledge increased across all health effects (p < 0.01 for all). Lastly, a significant majority of young adults reported they would purchase the branded pack without a HWL (39.5%), compared to all other pack types (p < 0.05 for all contrasts). The lowest proportion of young adults reported they would purchase a plain pack with a HWL (1.1%). CONCLUSIONS: Plain packaging and health warnings may reduce brand appeal and increase health knowledge among young adults.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".