Adult Smokers' Reactions to Pictorial Health Warning Labels on Cigarette Packs in Thailand and Moderating Effects of Type of Cigarette Smoked: Findings From the International Tobacco Control Southeast Asia Survey
Bibliographic record
Abstract
INTRODUCTION: In this study, we aimed to examine, in Thailand, the impact on smokers' reported awareness of and their cognitive and behavioral reactions following the change from text-only to pictorial warnings printed on cigarette packs. We also sought to explore differences by type of cigarette smoked (roll-your-own [RYO] vs. factory-made [FM] cigarettes). METHODS: Data came from the International Tobacco Control Southeast Asia Survey, conducted in Thailand and Malaysia, where a representative sample of 2,000 adult smokers from each country were recruited and followed up. We analyzed data from one wave before (Wave 1) and two waves after the implementation of the new pictorial warnings (two sets introduced at Waves 2 and 3, respectively) in Thailand, with Malaysia, having text-only warnings, serving as a control. RESULTS: Following the warning label change in Thailand, smokers' reported awareness and their cognitive and behavioral reactions increased markedly, with the cognitive and behavioral effects sustained at the next follow-up. By contrast, no significant change was observed in Malaysia over the same period. Compared to smokers who smoke any FM cigarettes, smokers of only RYO cigarettes reported a lower salience but greater cognitive reactions to the new pictorial warnings. CONCLUSIONS: The new Thai pictorial health warning labels have led to a greater impact than the text-only warning labels, and refreshing the pictorial images may have helped sustain effects. This finding provides strong support for introducing pictorial warning labels in low- and middle-income countries, where the benefits may be even greater, given the lower literacy rates and generally lower levels of readily available health information on the risks of 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".