Impact of graphic and text warnings on cigarette packs: findings from four countries over five years
Bibliographic record
Abstract
OBJECTIVES: To examine the impact of health warnings on smokers by comparing the short-term impact of new graphic (2006) Australian warnings with: (i) earlier (2003) United Kingdom larger text-based warnings; (ii) and Canadian graphic warnings (late 2000); and also to extend our understanding of warning wear-out. METHODS: The International Tobacco Control Policy Evaluation Survey (ITC Project) follows prospective cohorts (with replenishment) of adult smokers annually (five waves: 2002-2006), in Canada, United States, UK and Australia (around 2000 per country per wave; total n = 17 773). Measures were of pack warning salience (reading and noticing); cognitive responses (thoughts of harm and quitting); and two behavioural responses: forgoing cigarettes and avoiding the warnings. RESULTS: All four indicators of impact increased markedly among Australian smokers following the introduction of graphic warnings. Controlling for date of introduction, they stimulated more cognitive responses than the UK (text-only) changes, and were avoided more, did not significantly increase forgoing cigarettes, but were read and noticed less. The findings also extend previous work showing partial wear-out of both graphic and text-only warnings, but the Canadian warnings have more sustained effects than UK ones. CONCLUSIONS: Australia's new health warnings increased reactions that are prospectively predictive of cessation activity. Warning size increases warning effectiveness and graphic warnings may be superior to text-based warnings. While there is partial wear-out in the initial impact associated with all warnings, stronger warnings tend to sustain their effects for longer. These findings support arguments for governments to exceed minimum FCTC requirements on warnings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".