The association between smokers’ self-reported healthproblems and quitting: Findings from the ITC Four CountrySmoking and Vaping Wave 1 Survey
Bibliographic record
Abstract
INTRODUCTION: This study aimed to systematically examine whether having health conditions or concerns related to smoking are associated with quitting activities among smokers across four western countries. METHODS: Data came from the 2016 International Tobacco Control Four Country Smoking and Vaping Survey conducted in Australia, Canada, England and US. We asked smokers and recent quitters (n=11838) whether they had a medical diagnosis for heart disease, cancer, chronic lung disease, depression, anxiety, alcohol problems, diabetes, severe obesity and chronic pain (nine conditions), and whether they believed smoking had harmed/would harm their health, along with questions on quitting activities. RESULTS: General concerns about smoking harming health and all specific health conditions, except for alcohol problems, were positively associated with quit attempts, but the relationships between health conditions and other quitting measures (being abstinent, planning to quit, use of quitting medications) were less consistent. Positive associations between conditions and use of quitting medications were only significant for depression, anxiety and chronic pain (adjusted odds ratios ranged from 1.4 to 1.5). There was a general tendency to report lower self-efficacy for quitting among those with the health conditions. CONCLUSIONS: While those with smoking related conditions are somewhat more aware of the links to their smoking, and are largely taking more action, the extent of this is lower than one might reasonably expect. Enhanced awareness campaigns are needed and health professionals need to do more to use health conditions to motivate quit attempts and to ensure they are made with the most effective forms of help.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".