Quasi-experimentally examining the impact of introducing tobacco pictorial health warnings: Findings from the International Tobacco Control (ITC) 4C and Netherlands surveys in the Netherlands, Australia, Canada, United Kingdom, and the United States
Bibliographic record
Abstract
BACKGROUND: Our study evaluated the short-term impact of introducing European Union's tobacco pictorial health warnings (PHWs). METHODS: Longitudinal data were collected at two time-points from adult smokers, participating in the International Tobacco Control (ITC) surveys, conducted in the Netherlands, Australia, Canada, the United Kingdom and the United States. In the Netherlands, textual health warnings (THWs) were replaced by PHWs between both time-points. Health warning policies did not change in the other countries. Data from continuing smokers were used (N = 3,487) and analyzed using Generalized Estimating Equations. RESULTS: Between both time-points, only Dutch smokers showed increases in noticing health warnings (β = 0.712, p < 0.001), self-reports of health warnings leading to a cognitive response such as thinking about smoking health-risks (SHRs) (OR = 1.834, p < 0.001), knowledge about SHRs (β = 0.369, p < 0.001), and avoiding health warnings (OR = 9.869, p < 0.001). However, Dutch smokers showed no changes in attitude towards smoking (β = 0.035, p = 0.518), intention to quit smoking (OR = 0.791, p = 0.157), self-efficacy to quit smoking (β=-0.072, p = 0.286), or reporting that health warnings helped them to resist having a cigarette (OR = 1.091, p = 0.714). CONCLUSIONS: Results suggest that introducing the European PHWs was effective in provoking changes closely related to health warnings, but there was no direct impact on variables more closely related to smoking cessation.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".