Influence of Health Warnings on Beliefs about the Health Effects of Cigarette Smoking, in the Context of an Experimental Study in Four Asian Countries
Bibliographic record
Abstract
Cigarette package health warnings can be an important and low-cost means of communicating the health risks of smoking. We examined whether viewing health warnings in an experimental study influenced beliefs about the health effects of smoking, by conducting surveys with ~500 adult male smokers and ~500 male and female youth (age 16–18) in Beijing, China (n = 1070), Mumbai area, India (n = 1012), Dhaka, Bangladesh (n = 1018), and Republic of Korea (n = 1362). Each respondent was randomly assigned to view and rate pictorial health warnings for 2 of 15 different health effects, after which they reported beliefs about whether smoking caused 12 health effects. Respondents who viewed relevant health warnings (vs. other warnings) were significantly more likely to believe that smoking caused that particular health effect, for several health effects in each sample. Approximately three-quarters of respondents in China (Beijing), Bangladesh (Dhaka), and Korea (which had general, text-only warnings) thought that cigarette packages should display more health information, compared to approximately half of respondents in the Mumbai area, India (which had detailed pictorial warnings). Pictorial health warnings that convey the risk of specific health effects from smoking can increase beliefs and knowledge about the health consequences of smoking, particularly for health effects that are lesser-known.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".