Cessation assistance reported by smokers in 15 countries participating in the International Tobacco Control (ITC) policy evaluation surveys
Bibliographic record
Abstract
AIMS: To describe some of the variability across the world in levels of quit smoking attempts and use of various forms of cessation support. DESIGN: Use of the International Tobacco Control Policy Evaluation Project surveys of smokers, using the 2007 survey wave (or later, where necessary). SETTINGS: Australia, Canada, China, France, Germany, Ireland, Malaysia, Mexico, the Netherlands, New Zealand, South Korea, Thailand, United Kingdom, Uruguay and United States. PARTICIPANTS: Samples of smokers from 15 countries. MEASUREMENTS: Self-report on use of cessation aids and on visits to health professionals and provision of cessation advice during the visits. FINDINGS: Prevalence of quit attempts in the last year varied from less than 20% to more than 50% across countries. Similarly, smokers varied greatly in reporting visiting health professionals in the last year (<20% to over 70%), and among those who did, provision of advice to quit also varied greatly. There was also marked variability in the levels and types of help reported. Use of medication was generally more common than use of behavioural support, except where medications are not readily available. CONCLUSIONS: There is wide variation across countries in rates of attempts to stop smoking and use of assistance with higher overall use of medication than behavioural support. There is also wide variation in the provision of brief advice to stop by health professionals.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".