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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".