Perceptions of tobacco health warnings in China compared with picture and text-only health warnings from other countries: an experimental study
Bibliographic record
Abstract
OBJECTIVE: To assess the perceived effectiveness of cigarette health warnings in China, compared with picture and text-only warnings from other countries. METHOD: 1169 individuals (adult smokers, adult nonsmokers and youth) from four Chinese cities (Beijing, Shanghai, Kunming and Yinchuan) viewed 10 health warnings on cigarette packages, which included (a) the current Chinese text warnings covering 30% of the front/back of the pack (introduced October 2008); (b) the former Chinese text warning located on the side of the pack; (c) four picture warnings covering 50% of the front/back of the pack from Canada (lung cancer), Singapore (mouth disease), Hong Kong (gangrene) and European Union (clogged arteries); and (d) the same four warnings without the picture. Participants rated and ranked the 10 warnings on dimensions including how effective each would be in motivating smokers to quit and in convincing youth not to start smoking. RESULTS: Both Chinese warnings were consistently rated as least effective, with the new Chinese warning rated only slightly higher than the old warning. The picture warnings were consistently ranked or rated as most effective, with the text-only versions in the middle. Results were consistent across subject group, city and sex. CONCLUSIONS: (1) Picture warnings are rated as much more effective than the same warnings without pictures. (2) The revised health warnings in China, introduced in October 2008, are only marginally more effective than the previous warning and far less effective than even text warnings from other countries. These results, coupled with population-based evaluation studies, suggest that pictorial warnings would significantly increase the impact of health warnings in China.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".