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 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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".