Promoting cessation resources through cigarette package warning labels: a longitudinal survey with adult smokers in Canada, Australia and Mexico
Bibliographic record
Abstract
INTRODUCTION: Health warning labels (HWLs) on tobacco packaging can be used to provide smoking cessation information, but the impact of this information is not well understood. METHODS: Online consumer panels of adult smokers from Canada, Australia and Mexico were surveyed in September 2012, January 2013 and May 2013; replenishment was used to maintain sample sizes of 1000 participants in each country at each wave. Country-stratified logistic Generalised Estimating Equation (GEE) models were estimated to assess correlates of citing HWLs as a source of information on quitlines and cessation websites. GEE models also regressed having called the quitline, and having visited a cessation website, on awareness of these resources because of HWLs. RESULTS: At baseline, citing HWLs as a source of information about quitlines was highest in Canada, followed by Australia and Mexico (33%, 19% and 16%, respectively). Significant increases over time were only evident in Australia and Mexico. In all countries, citing HWLs as a source of quitline information was significantly associated with self-report of having called a quitline. At baseline, citing HWLs as a source of information about cessation websites was higher in Canada than in Australia (14% and 6%, respectively; Mexico was excluded because HWLs do not include website information), but no significant changes over time were found for either country. Citing HWLs as a source of information about cessation websites was significantly associated with having visited a website in both Canada and Australia. CONCLUSIONS: HWLs are an important source of cessation information.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".