Cigarette packet warning labels can prevent relapse: findings from the International Tobacco Control 4-Country policy evaluation cohort study
Bibliographic record
Abstract
OBJECTIVES: To investigate the links between health warning labels (WLs) on cigarette packets and relapse among recently quit smokers. DESIGN: Prospective longitudinal cohort survey. SETTING: Australia, Canada, the UK and the USA. PARTICIPANTS: 1936 recent ex-smokers (44.4% male) from one of the first six waves (2002-2007) of the International Tobacco Control 4-Country policy evaluation survey, who were followed up in the next wave. MAIN OUTCOME MEASURES: Whether participants had relapsed at follow-up (approximately 1 year later). RESULTS: In multivariate analysis, very frequent noticing of WLs among ex-smokers was associated with greater relapse 1 year later (OR: 1.52, 95% CI 1.11 to 2.09, p<0.01), but this effect disappeared after controlling for urges to smoke and self-efficacy (OR: 1.29, 95% CI 0.92 to 1.80, p=0.135). In contrast, reporting that WLs make staying quit 'a lot' more likely (compared with 'not at all' likely) was associated with a lower likelihood of relapse 1 year later (OR: 0.65, 95% CI 0.49 to 0.86, p<0.01) and this effect remained robust across all models tested, increasing in some. CONCLUSIONS: This study provides the first longitudinal evidence that health warnings can help ex-smokers stay quit. Once the authors control for greater exposure to cigarettes, which is understandably predictive of relapse, WL effects are positive. However, it may be that ex-smokers need to actively use the health consequences that WLs highlight to remind them of their reasons for quitting, rather than it being something that happens automatically. Ex-smokers should be encouraged to use pack warnings to counter urges to resume smoking. Novel warnings may be more likely to facilitate this.
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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 | 0.003 | 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) | 0.001 | 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 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".