Evaluating Canada's innovative policy for health warnings on cigarette sticks: A pre/post assessment among adults who smoke
Bibliographic record
Abstract
OBJECTIVE: Evaluate Canada's innovative policy mandating warning messages on cigarette sticks. METHODS: We analyzed data from an open cohort of Canadian adults who smoke, surveyed every 3 months (February 2023-November 2024; n = 11,487 observations from 4716 individuals). Participants reported: liking the look of their cigarette sticks (1-Dislike a lot to 7-Like a lot); feelings when looking at sticks (1-Very bad to 7-Very good); frequency of thinking about smoking-related harms due to sticks (1-Not at all to 5-Extremely); and forgoing cigarettes they normally smoke due to the look of sticks (no vs. yes). Linear and logistic generalized estimating equations regressed these outcomes on implementation period (i.e., pre-policy 2023 surveys [ref.] vs post-policy 2024 surveys), adjusting for covariates and post-stratification weights. Analyzing participants followed to the next survey (n = 6959 observations, 2356 individuals), separate adjusted mixed-effects logistic models regressed quit attempts in the 3-month interval since the prior survey on each stick measure from the prior survey (coded: neutral [ref.], dislike, like; neutral [ref.], bad, good; no forgoing [ref.], forwent cigarettes). RESULTS: From pre- to post-policy periods, liking and feelings about sticks became more negative (B = -0.15, 95 %CI = -0.22, -0.08; B = -0.07, 95 %CI = -0.13, -0.01) and forgoing cigarettes increased (AOR = 1.18, 95 %CI = 1.06, 1.32). Those who felt bad (vs. neutral) when looking at sticks were more likely to try to quit by the next survey (AOR = 1.31, 95 %CI = 1.05, 1.62), as were those who forwent cigarettes (AOR = 1.73, 95 %CI = 1.40, 2.15). CONCLUSIONS: Countries should consider expanding cigarette labeling to include on-cigarette warnings, which appear to have increased outcomes that predict 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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".