Mediational pathways of the impact of cigarette warning labels on quit attempts.
Bibliographic record
Abstract
OBJECTIVE: To test and develop, using structural equation modeling, a robust model of the mediational pathways through which health warning labels exert their influence on smokers' subsequent quitting behavior. METHOD: Data come from the International Tobacco Control Four-Country Survey, a longitudinal cohort study conducted in Australia, Canada, the United Kingdom, and the United States. Waves 5-6 data (n = 4,988) were used to calibrate the hypothesized model of warning label impact on subsequent quit attempts via a set of policy-specific and general psychosocial mediators. The finalized model was validated using Waves 6-7 data (n = 5065). RESULTS: As hypothesized, warning label salience was positively associated with thoughts about risks of smoking stimulated by the warnings (β = .58, p < .001), which in turn were positively related to increased worry about negative outcomes of smoking (β = .52, p < .001); increased worry in turn predicted stronger intention to quit (β = .39, p < .001), which was a strong predictor of subsequent quit attempts (β = .39, p < .001). This calibrated model was successfully replicated using Waves 6-7 data. CONCLUSION: Health warning labels seem to influence future quitting attempts primarily through their ability to stimulate thoughts about the risks of smoking, which in turn help to raise smoking-related health concerns, which lead to stronger intentions to quit, a known key predictor of future quit attempts for smokers. By making warning labels more salient and engaging, they should have a greater chance to change behavior.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".