Changes in smokers' responses to novel efficacy messages inside cigarette packages following Canada's 2024 labeling policy update: A pre-post longitudinal study
Bibliographic record
Abstract
OBJECTIVE: Evaluate the impact of Canada's innovative inside-pack efficacy messages about cessation benefits and tips to quit, whose content was updated in 2024. METHODS: We analyzed data from an open cohort of Canadian adults who smoke, surveyed every three months from February 2023 to November 2024 (n = 12,022 observations, 4716 individuals). At each survey, participants reported the frequency of reading health messages inside packs in the past 30 days (Never/Rarely = reference vs Sometimes/Often/Very often); perceived cessation benefits from inside-pack messages (Not at all-Extremely); forgoing cigarettes due to inside-pack messages in the prior 30 days (No = reference vs Yes); and confidence/self-efficacy to quit smoking (Not at all-Extremely). Linear and logistic generalized estimating equation models regressed these outcomes on implementation period (pre- vs post-implementation surveys). Analyzing participants followed to the subsequent survey (n = 6959 observations, 2356 individuals), mixed-effects logistic models regressed quit attempts in the three-month interval since the prior survey on message responses from the prior survey. All models adjusted for sociodemographics, smoking-related variables, and post-stratification weights. RESULTS: Self-reported reading inside-pack messages (OR = 1.18; 95 %CI = 1.04, 1.34), perceived cessation benefits (β = 0.07; 95 %CI = 0.01, 0.12), forgoing cigarettes (OR = 1.14; 95 %CI = 1.01, 1.28), and self-efficacy (β = 0.08; 95 %CI = 0.04, 0.13) all increased from pre-to post-implementation. Participants who reported reading messages more frequently (OR = 1.54; 95 %CI = 1.09-2.00), perceived greater cessation benefits (OR = 1.31; 95 %CI = 1.22, 1.42), forwent cigarettes (OR = 1.88; 95 %CI = 1.48, 2.37) and had greater self-efficacy (OR = 1.32; 95 %CI = 1.19, 1.47) were more likely to quit at followup. CONCLUSIONS: After Canada implemented new efficacy messages inside packs, message engagement and predictors of cessation behaviors increased. Other countries may consider similar policies.
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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 | 0.001 | 0.002 |
| 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".