Assessing the impact of cigarette package health warning labels: a cross-country comparison in Brazil, Uruguay and Mexico
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of different health warning labels (HWL). MATERIAL AND METHODS: Data from the International Tobacco Control Survey (ITC Survey) were analyzed from adult smokers in Brazil, Uruguay and Mexico, each of which used a different HWL strategy (pictures of human suffering and diseased organs; abstract pictorial representations of risk; and text-only messages, respectively). Main outcomes were HWL salience and cognitive impact. RESULTS: HWLs in Uruguay (which was the only country with a HWL on the front of the package) had higher salience than either Brazilian or Mexican packs. People at higher levels of educational attainment in Mexico were more likely to read the text-only HWLs whereas education was unassociated with salience in Brazil or Uruguay. Brazilian HWLs had greater cognitive impacts than HWLs in either Uruguay or Mexico. HWLs in Uruguay generated lower cognitive impacts than the text-only HWLs in Mexico. In Brazil, cognitive impacts were strongest among smokers with low educational attainment. CONCLUSIONS: This study suggests that HWLs have the most impact when they are prominent (i.e., front and back of the package) and include emotionally engaging imagery that illustrates negative bodily impacts or human suffering due to smoking.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".