Which type of tobacco product warning imagery is more effective and sustainable over time
Bibliographic record
Abstract
Objective This study examined smokers’ responses to pictorial health warnings (PHWs) with different types of imagery under natural exposure conditions. Methods Adult smokers from online panels in Canada (n=2357), Australia (n=1671) and Mexico (n=2537) were surveyed every 4 months from 2012 to 2013. Participants were shown PHWs on packs in their respective countries and asked about: (1) noticing PHWs; (2) negative affects towards PHWs; (3) believability of PHWs; (4) PHW-stimulated discussions; and (5) quit motivation due to PHWs. Country-specific generalised estimating equation models regressed these outcomes on time (ie, survey wave), PHW imagery type (ie, symbolic representations of risk, suffering from smoking and graphic depictions of bodily harm) and interactions between them. Results In all countries, PHW responses did not significantly change over time, except for increased noticing PHWs in Canada and Mexico, increased negative affect in Australia and decreased negative affect in Mexico. For all outcomes, symbolic PHWs were rated lower than suffering and graphic PHWs in Canada (the only country with symbolic PHWs). Graphic PHWs were rated higher than suffering PHWs for negative affect (all countries), discussions (Canada) and quit motivation (Australia). Suffering PHWs were rated higher than graphic PHWs for noticing PHWs (Canada), believability (all countries), discussions (AustraliaandMexico) and quit motivation (Mexico). Changes in noticing, believability and discussions varied somewhat by imagery type across countries. Conclusions The different PHW imagery appears to have different pathways of influence on adult smokers. Reactions to specific PHWs are similar over 1–2 years, suggesting that wear-out of PHW effects is due to decreased attention rather than the diminishing effectiveness of content.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 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".