Pictorial Health Warnings on Cigarette Packs in the United States: An Experimental Evaluation of the Proposed FDA Warnings
Bibliographic record
Abstract
INTRODUCTION: In 2010, the U.S. Food and Drug Administration (FDA) developed 36 proposed health warnings for cigarette packages, from which 9 were subsequently selected for implementation. The current study aimed to evaluate the perceived efficacy of the 36 proposed FDA warnings. METHODS: Web-based surveys were conducted with 783 adult smokers and 510 youth in United States. Participants were randomized to view and rate two sets of 6-7 warnings, each set corresponding to one of nine health effect statements required under the Tobacco Control Act. Warnings included all 36 FDA-proposed warnings and additional warnings for comparison. RESULTS: Youth and adults rated individual warnings similarly; in all cases where differences were found, youth perceived warnings as more effective. Comparisons on specific elements indicated that warnings were perceived as more effective if they were: full color (vs. black and white), featured real people (vs. comic book style), contained graphic images (vs. nongraphic), and included a telephone "quitline" number or personal information. Few sociodemographic differences were observed in overall perceived effectiveness: younger respondents, non-White respondents, and smokers intending to quit rated warnings higher. CONCLUSIONS: Seven of the nine health warnings selected by the FDA for implementation were among the proposed warnings rated as most effective in the current study. However, the warning(s) added for comparison were rated higher than the FDA-selected warning for five of the nine sets, suggesting some warnings could be improved for greater impact. The findings support the inclusion of a telephone "quitline" number and reinforce the importance of depicting "real" people and health effects.
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 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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".