The Differential Impact of the 2000 Canadian Graphic Warning Label Policy on Smoking Prevalence by Sex and Education: A Difference-in-Difference-in-Difference Model
Bibliographic record
Abstract
INTRODUCTION: Using a quasiexperimental design, we compared the impact of the 2000 Canadian introduction of graphic warning labels (GWLs) on differences in smoking prevalence by sex and education, to the United States, where no GWLs were introduced. METHODS: We pooled 1999-2004 data from the Canadian Tobacco Use Monitoring Survey and the U.S. Behavioral Risk Factor Surveillance System. We used a difference-in-difference (DD) model to assess the impact of Canadian policy introduction on smoking prevalence, and a difference-in-difference-in-difference (DDD) model to examine differences in the policy impact by sex and education, comparing Canada (the treatment group) with the United States (the control group). RESULTS: From 1999 to 2004, smoking prevalence decreased from 23.7% to 18.6% in Canada, and from 21.7% to 20.0% in the United States. Results from the DD regression models showed that Canadian respondents reported lower odds of being a current smoker compared to the U.S. respondents following the 2000 introduction of GWLs (OR = 0.84, 95% CI = 0.74-0.94). The DDD model showed that the impact of the Canadian GWLs versus the United States did not differ by sex or education. CONCLUSIONS: The 2000 Canadian GWL policy reduced smoking prevalence overall, with similar reductions for males and females and across education levels. The impact of the Canadian GWLs in reducing smoking prevalence did not reduce differences by sex or education. Although beneficial for all smokers, GWLs may not serve to decrease existing disparities, especially those by socioeconomic status. IMPLICATIONS: Existing evidence shows that GWL implementation is associated with reductions in smoking prevalence. But there is limited evidence from past evaluation studies on whether the impact of GWLs on smoking prevalence differs by sociodemographic subgroup. Our findings confirm existing studies that the 2000 implementation of GWLs in Canada was significantly associated with an overall reduction in smoking prevalence in Canada compared to the United States. However, our study improves existing evidence by showing that the impact of the Canadian GWLs on smoking prevalence did not differ by sex or education, and thus did not reduce existing smoking disparities by educational levels.
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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 | 0.060 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".