How reactions to cigarette packet health warnings influence quitting: findings from the ITC Four‐Country survey
Bibliographic record
Abstract
OBJECTIVES: To examine prospectively the impact of health warnings on quitting activity. DESIGN: Five waves (2002-06) of a cohort survey where reactions to health warnings at one survey wave are used to predict cessation activity at the next wave, controlling for country (proxy for warning differences) and other factors. These analyses were replicated on four wave-to-wave transitions. SETTING AND PARTICIPANTS: Smokers from Australia, Canada, the United Kingdom and the United States. Samples were waves 1-2: n = 6525; waves 2-3: n = 5257; waves 3-4: n = 4439; and waves 4-5: n = 3993. MEASURES: Warning salience, cognitive responses (thoughts of harm and of quitting), forgoing of cigarettes and avoidance of warnings were examined as predictors of quit attempts, and of quitting success among those who tried (1 month sustained abstinence), replicated across four wave-to-wave transitions. RESULTS: All four responses to warnings were independently predictive of quitting activity in bivariate analyses. In multivariate analyses, both forgoing cigarettes and cognitive responses to the warnings predicted prospectively making quit attempts in all replications. However, avoiding warnings did not add predictive value consistently, and there was no consistent pattern for warning salience. There were no interactions by country. Some, but not all, the effects were mediated by quitting intentions. There were no consistent effects on quit success. CONCLUSIONS: This study adds to the evidence that forgoing cigarettes as a result of noticing warnings and quit-related cognitive reactions to warnings are consistent prospective predictors of making quit attempts. This work strengthens the evidence base for governments to go beyond the Framework Convention on Tobacco Control to mandate health warnings on tobacco products that stimulate the highest possible levels of these reactions.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".