Impact of Graphic Pack Warnings on Adult Smokers’ Quitting Activities: Findings from the ITC Southeast Asia Survey (2005–2014)
Bibliographic record
Abstract
Malaysia introduced graphic health warning labels (GHWLs) on all tobacco packages in 2009. We aimed to examine if implementing GHWLs led to stronger warning reactions (e.g., thinking about the health risks of smoking) and an increase in subsequent quitting activities; and to examine how reactions changed over time since the implementation of the GHWLs in Malaysia and Thailand where GHWL size increased from 50-55% in 2010. Data came from six waves (2005-2014) of the International Tobacco Control Southeast Asia Survey. Between 3,706 and 4,422 smokers were interviewed across these two countries at each survey wave. Measures included salience of warnings, cognitive responses (i.e., thinking about the health risks and being more likely to quit smoking), forgoing cigarettes, and avoiding warnings. The main outcome was subsequent quit attempts. Following the implementation of GHWLs in Malaysia, reactions increased, in some cases to levels similar to the larger Thai warnings, but declined over time. In Thailand, reactions increased following implementation, with no decline for several years, and no clear effect of the small increase in warning size. Reactions, mainly cognitive responses, were consistently predictive of quit attempts in Thailand, but this was only consistently so in Malaysia after the change to GHWLs. In conclusion, GHWLs are responded to more frequently, and generate more quit attempts, but warning wear-out is not consistent in these two countries, perhaps due to differences in other tobacco control efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".