Impact of adding and removing warning label messages from cigarette packages on adult smokers’ awareness about the health harms of smoking: findings from the ITC Canada Survey
Bibliographic record
Abstract
INTRODUCTION: removing messages from cigarette HWLs on smokers' awareness of harms. METHODS: Data were drawn from nine waves of the International Tobacco Control (ITC) Canada Survey, a national representative cohort of adult smokers (n=5863) conducted nearly annually between 2002 and 2013-2014. Two analytical approaches were conducted: generalised estimating equation (GEE) regression models estimated adjusted percentages of correct smoking-related health statements at each wave and segmented regression analyses modelled temporal trends in awareness before and after the revisions by measuring the difference in slopes. RESULTS: Adding messages to HWLs significantly increased awareness that smoking causes blindness (OR=3.36 (95% CI 2.71 to 4.18); p<0.001; estimated increase of 1.01 million smokers in Canada) and bladder cancer (OR=2.14 (95% CI 1.71 to 2.66), p<0.001; estimated increase of 1.09 million smokers). Adding the warning that nicotine causes addiction did not significantly impact smokers' awareness. Removing messages was shown to decrease awareness that cigarette smoke contains carbon monoxide (OR=0.53 (95% CI 0.41 to 0.70), p<0.001; estimated decrease of 342 000 smokers) and smoking causes impotence (p=0.007 for the difference in slopes; estimated decrease of 354 000 smokers). CONCLUSIONS: Adding messages to HWLs increases smokers' awareness of health facts, but removing messages decreases awareness. These findings demonstrate the importance of carefully considering the implications of adding and especially removing messages from HWLs and the importance of regularly revising warnings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".