Disparagement of health warning labels on cigarette packages and cessation attempts: results from four countries
Bibliographic record
Abstract
Health warning labels (HWLs) on cigarette packs that use strong fear appeals may evoke defensive responses including acts of disparaging the warnings. Whether warning disparagement undermines HWL effectiveness remains unclear. We assessed correlates of one type of HWL disparagement and its association with subsequent cessation attempts. Longitudinal data (2012-14) on adult smokers from Australia, Canada, Mexico and the United States (US) were analyzed. HWL disparagement was assessed as the frequency of making fun of HWLs in the past month. Using Generalized Estimating Equation models we estimated correlates of HWL disparagement and whether HWL disparagement predicted subsequent cessation attempts. In each country, across all waves, 24-31% of smokers reported making fun of the warnings at least once in the past month. More frequent disparagement was found among males, younger participants, those with higher education and greater addiction, and those who recently attempted to quit. Attention to, avoidance of and talking to others about HWLs were all positively associated with HWL disparagement. In all countries, except the US, this type of HWL disparagement was an independent predictor of subsequent cessation attempts. HWL disparagement among smokers may indicate greater warning relevance and processing and does not result in counterproductive effects on cessation efforts.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".