Effectiveness of the European Union text-only cigarette health warnings: findings from four countries
Bibliographic record
Abstract
BACKGROUND: The European Commission requires tobacco products sold in the European Union to display standardized text health warnings. This article examines the effectiveness of the text health warnings among daily cigarette smokers in four Member States. METHODS: Data were drawn from nationally representative samples of smokers from the International Tobacco Control Policy Evaluation Project surveys in France (2007), Germany (2007), the Netherlands (2008) and the UK (2006). We examined: (i) smokers' ratings of the health warnings on warning salience, thoughts of harm and quitting and forgoing of cigarettes; (ii) impact of the warnings using a Labels Impact Index (LII), with higher scores signifying greater impact; and (iii) differences on the LII by demographic characteristics and smoking behaviour. RESULTS: Scores on the LII differed significantly across countries. Scores were highest in France, lower in the UK, and lowest in Germany and the Netherlands. Across all countries, scores were significantly higher among low-income smokers, smokers who had made a quit attempt in the past year and smokers who smoked fewer cigarettes per day. CONCLUSION: The impact of the health warnings varies greatly across countries. Impact tended to be highest in countries with more comprehensive tobacco control programmes. Because the impact of the warnings was highest among smokers with the lowest socioeconomic status (SES), this research suggests that health warnings could be more effective among smokers from lower SES groups. Differences in warning label impact by SES should be further investigated.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".