Does Adding Information on Toxic Constituents to Cigarette Pack Warnings Increase Smokers’ Perceptions About the Health Risks of Smoking? A Longitudinal Study in Australia, Canada, Mexico, and the United States
Bibliographic record
Abstract
BACKGROUND: Health warning labels (HWLs) on cigarette packs in Australia, Canada, Mexico, and the United States include varying information about toxic cigarette smoke constituents and smoking-related health risks. HWL information changed more recently in Australia, Canada, and Mexico than in the United States. AIMS: To investigate whether smokers' knowledge of toxic constituents and perceived smoking-related risks increased after adding this information to HWLs and how knowledge of toxic constituents is associated with perceptions of smoking-related risks. METHODS: Data come from a longitudinal, online cohort of 4,621 adult smokers surveyed every 4 months from September 2012 (Wave 1) to January 2014 (Wave 5) in Australia, Canada, and Mexico, with the United States being surveyed from Waves 2 to 5. Generalized estimating equation models estimated the association between perceived smoking-related risk at follow-up and prior wave knowledge of toxic constituents, adjusting for attention to HWLs, sociodemographics, and smoking-related characteristics. RESULTS: Between 2012 and 2014, knowledge of toxic constituents increased in Australia, Canada, and Mexico ( p < .001), but not in the United States. Higher levels of both attention to HWLs and knowledge of toxic constituents were associated with a higher perceived risk of smoking-related conditions at follow-up across all countries except for the United States. CONCLUSIONS: Our results suggest that information about toxic constituents on prominent HWLs not only increases smoker's knowledge of toxic constituents, but that it may also reinforce the effects of HWL messages about specific, smoking-related health outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".