Cigarette packaging and health warnings: the impact of plain packaging and message framing on young smokers
Bibliographic record
Abstract
OBJECTIVE: This study examined the impact of pictorial cigarette-warning labels, warning-label message framing and plain cigarette packaging, on young adult smokers' motivation to quit. METHODS: Smokers aged 18-30 years (n=740) from a consumer research panel were randomised to one of four experimental conditions where they viewed online images of four cigarette packs with warnings about lung disease, cancer, stroke/heart disease and death, respectively. Packs differed across conditions by warning-message framing (gain vs loss) and packaging (branded vs plain). Measures captured demographics, smoking behaviour, covariates and motivation to quit in response to cigarette packs. RESULTS: Pictorial warnings about lung disease and cancer generated the strongest motivation to quit across conditions. Adjusting for pretest motivation and covariates, a message framing by packaging interaction revealed gain-framed warnings on plain packs generated greater motivation to quit for lung disease, cancer and mortality warnings (p<0.05), compared with loss-framed warnings on plain packs. CONCLUSIONS: Warnings combining pictorial depictions of smoking-related health risks with text-based messages about how quitting reduces risks, may achieve better outcomes among young adults, especially in countries considering or implementing plain packaging regulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".