Exploring the effectiveness of cigarette warning labels: findings from the United States and United Kingdom arms of the International Tobacco Control (ITC) Four Country Survey
Bibliographic record
Abstract
Abstract This paper explores the effectiveness of cigarette warning labels across two countries, one (the UK) with new and stricter legislation where text based labels have been made more prominent and one (the USA) with less stringent regulation, where labels are less visible. Using longitudinal data from the two countries, the research seeks to investigate the impact of the different types of warning labels on the information processing by consumers. This paper assesses the effectiveness of warning labels in terms of: consumer attention, elaboration, contemplation on quitting and behavioural compliance. This study provides a comprehensive examination of these key factors in a fixed causal sequence. Structural equation modelling was used to test this model based on longitudinal panel survey data from the International Tobacco Control (ITC) Four Country Survey. Analysis of a sample of 901 US smokers and 1459 UK smokers yielded results in full support of all hypothesised relationships in the model proposed for both countries. Findings suggest that the new European Union policy of more prominent warning labels has a direct effect on influencing behavioural compliance by smokers. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".