Impact of the removal of light and mild descriptors from cigarette packages in Ontario, Canada: Switching to “light replacement” brand variants
Bibliographic record
Abstract
OBJECTIVE: This study assessed cessation and brand switching among smokers in Ontario, Canada after tobacco companies' voluntary removal of 'light' and 'mild' descriptors from cigarette packages. METHOD: We analyzed longitudinal data on brand preference and cessation from a cohort of smokers (n=632) in the Ontario Tobacco Survey in Canada from 2006 to 2008 with a longitudinal regression model. RESULTS: While cessation differed by brand variant prior to the ban (7% light vs. 3% regular; P<0.05), it did not differ by brand variant after the ban was implemented. In 2008, when light cigarette brand variants were no longer available, 33% of the sample still reported smoking lights and 31% smoked light replacement brand variants. During each subsequent follow-up, light brand smokers had 2 times the odds of smoking regular brand variants (Adjusted OR: 2.03, 95% CI 1.80,2.29), and almost 5 times the odds of using light replacement brand variants (Adjusted OR: 4.87, 95% CI 4.07,5.84), respectively, compared to continuing to smoke lights. CONCLUSIONS: Even after removing misleading descriptors from cigarette packs, smokers continued to report using light brand variants, and many switched to newly introduced light replacement brand variants. After full implementation of the ban, cessation did not vary by brand variant.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".