Predictive and External Validity of a Pre-Market Study to Determine the Most Effective Pictorial Health Warning Label Content for Cigarette Packages
Bibliographic record
Abstract
INTRODUCTION: Studies examining cigarette package pictorial health warning label (HWL) content have primarily used designs that do not allow determination of effectiveness after repeated, naturalistic exposure. This research aimed to determine the predictive and external validity of a pre-market evaluation study of pictorial HWLs. METHODS: Data were analyzed from: (1) a pre-market convenience sample of 544 adult smokers who participated in field experiments in Mexico City before pictorial HWL implementation (September 2010); and (2) a post-market population-based representative sample of 1765 adult smokers in the Mexican administration of the International Tobacco Control Policy Evaluation Survey after pictorial HWL implementation. Participants in both samples rated six HWLs that appeared on cigarette packs, and also ranked HWLs with four different themes. Mixed effects models were estimated for each sample to assess ratings of relative effectiveness for the six HWLs, and to assess which HWL themes were ranked as the most effective. RESULTS: Pre- and post-market data showed similar relative ratings across the six HWLs, with the least and most effective HWLs consistently differentiated from other HWLs. Models predicting rankings of HWL themes in post-market sample indicated: (1) pictorial HWLs were ranked as more effective than text-only HWLs; (2) HWLs with both graphic and "lived experience" content outperformed symbolic content; and, (3) testimonial content significantly outperformed didactic content. Pre-market data showed a similar pattern of results, but with fewer statistically significant findings. CONCLUSIONS: The study suggests well-designed pre-market studies can have predictive and external validity, helping regulators select HWL content.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".