Do post‐quitting experiences predict smoking relapse among former smokers in Australia and the United Kingdom? Findings from the International Tobacco Control Surveys
Bibliographic record
Abstract
INTRODUCTION: Many smokers attempt to stop smoking every year, but the vast majority of quit attempts fail. This study examined prospectively the association between post-quitting experiences and smoking relapse among ex-smokers in Australia and the United Kingdom. METHODS: Data came from 584 adult ex-smokers from Australia and the United Kingdom who participated in Wave 9 of the International Tobacco Control Four Country Survey and successfully followed up a year later (Wave 10). Binary logistic regression was used to examine whether baseline post-quitting experiences predicted relapse back to smoking at follow-up. RESULTS: Ex-smokers who perceived their stress coping ability had gotten worse since quitting were more likely to relapse back to smoking compared to their counterparts who reported no change (odds ratio = 5.77, 95% confidence interval = 1.64, 20.31, P < 0.01). Ex-smokers who reported their homes had become fresher and cleaner post quitting were less likely to relapse compared to those who did not notice any change (odds ratio = 0.34, 95% confidence interval = 0.13, 0.93, P < 0.05). Perceived changes in life enjoyment, negative affect control, social confidence, work performance, leisure time and financial situation did not independently predict relapse. No country differences were found. DISCUSSION AND CONCLUSIONS: The study showed that ex-smokers' relapse risk was elevated if they perceived any negative impact of quitting on their stress coping whereas relapse risk was reduced if they perceived any positive impact of quitting on the home (e.g. fresher and cleaner). Helping ex-smokers to develop alternative stress coping strategies and highlighting the positive impacts of quitting smoking on the homes may help protect against smoking relapse.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".