The Effect of Graphic Cigarette Warning Labels on Smoking Behavior: Evidence from the Canadian Experience
Bibliographic record
Abstract
INTRODUCTION: There is a substantial literature that graphic tobacco warnings are effective; however, there is limited evidence based on actual smoking behavior. The objective of this paper is to assess the effect of graphic cigarette warning labels on smoking prevalence and quit attempts. METHODS: A nationally representative sample of individuals aged 15 years and older from the Canadian National Population Health Survey 1998-2008 is used. The sample consists of 4,853 individuals for the smoking prevalence regression and 1,549 smokers for quit attempts. The generalized estimating equation (GEE) model was used to examine the population-averaged (marginal) effects of tobacco graphic warnings on smoking prevalence and quit attempts. To assess the effect of graphic tobacco health warnings on smoking behavior, we used a scaled variable that takes the value of 0 for the first 6 months in 2001, then increases gradually to 1 from December 2001. RESULTS: We found that graphic warnings had a statistically significant effect on smoking prevalence and quit attempts. In particular, the warnings decreased the odds of being a smoker (odds ratio [OR] = 0.875; 95% CI = 0.821-0.932) and increased the odds of making a quit attempt (OR = 1.330, CI = 1.187-1.490). Similar results were obtained when we allowed for more time for the warnings to appear in retail outlets. CONCLUSION: This study adds to the growing body of evidence on the effectiveness of graphic warnings. Our findings suggest that warnings had a significant effect on smoking prevalence and quit attempts in Canada.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".