Impact of the graphic Canadian warning labels on adult smoking behaviour
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of graphic Canadian cigarette warning labels on current adult smokers. DESIGN: A random-digit-dial telephone survey was conducted with 616 adult smokers in south western Ontario, Canada in October/November 2001, with three month follow up. MAIN OUTCOME MEASURES: Smoking behaviour (quitting, quit attempts, and reduced smoking), intentions to quit, and salience of the warning labels. RESULTS: Virtually all smokers (91%) reported having read the warning labels and smokers demonstrated a thorough knowledge of their content. A strong positive relation was observed between a measure of cognitive processing-the extent to which smokers reported reading, thinking about, and discussing the new labels-and smokers' intentions to quit (odds ratio (OR) 1.11, 95% confidence interval (CI) 1.07 to 1.16; p < 0.001). Most important, cognitive processing predicted cessation behaviour at follow up. Smokers who had read, thought about, and discussed the new labels at baseline were more likely to have quit, made a quit attempt, or reduced their smoking three months later, after adjusting for intentions to quit and smoking status at baseline (OR 1.07, 95% CI 1.03 to 1.12; p < 0.001). CONCLUSIONS: Graphic cigarette warning labels serve as an effective population based smoking cessation intervention. The findings add to the growing literature on health warnings and provide strong support for the effectiveness of Canada's tobacco labelling policy.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".