The Use of Cigarette Package Inserts to Supplement Pictorial Health Warnings: An Evaluation of the Canadian Policy
Bibliographic record
Abstract
BACKGROUND: Canada is the first country in the world to require cigarette manufacturers to enclose package inserts to supplement the exterior pictorial health warning label (HWL). In June 2012, Canada implemented new HWL package inserts that include cessation tips accompanied by a pictorial image. This study aims to assess the extent to which adult smokers report reading the newly mandated HWL inserts and to see whether reading them is associated with making a quit attempt. METHODS: Data were analyzed from an online consumer panel of Canadian adult smokers, aged 18-64 years. Five waves of data were collected between September 2012 and January 2014, separated by 4-months intervals (n = 1,000 at each wave). Logistic generalized estimating equation (GEE) models were estimated to assess correlates of reading inserts and whether doing so is associated with making a quit attempt by the subsequent wave. RESULTS: At each wave, between 26% and 31% of the sample reported having read HWL package inserts at least once in the prior month. Smokers who read them were more likely to be younger, female, have higher income, intend to quit, have recently tried to quit, and thought more frequently about health risks because of warning labels. In models that adjusted for these and other potential confounders, smokers who read the inserts a few times or more in the past month were more likely to make a quit attempt at the subsequent wave compared to smokers who did not read the inserts. CONCLUSIONS: HWL package inserts with cessation-related tips and messages appear to increase quit attempts made by smokers.
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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 | 0.031 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".