Mediational pathways of the impact of cigarette warning labels on quit attempts.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| 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 it