Experienced Effects on Well-Being following Smoking Cessation: Findings from the 2020 ITC Four Country Smoking and Vaping Survey
Bibliographic record
Abstract
Background and Aims: There has been limited research addressing changes in subjective well-being as a result of quitting smoking. This paper examines recent ex-smokers’ well-being related experiences overall and as a function of (1) duration of cessation and (2) continued nicotine use from vaping. Methods: A sample of 1379 ever-daily smoking ex-smokers (quit for up to 5 years) from the 2020 ITC Four Country Smoking and Vaping Survey (Australia, Canada, the UK, and the US), of which 27.1% currently vaped daily. Well-being measures were perceived changes post-quitting in emotion coping (stress and negative emotions), enjoyment of life, and day-to-day functioning. We also assessed the level of persisting worry about past smoking leading to future health problems. Results: Overall, among those answering all four well-being measures, 51.8% of the ex-smokers reported positive effects and no negatives, but 27.3% reported at least one negative effect, with the remainder reporting no change in any measure. Positive effects were greater among those who had quit more than 1 year prior. The largest improvement (56.3%) was for daily functioning, which showed improvement over time since having quit. Current daily vapers reported similar well-being as those not vaping; however, fewer daily vapers reported worsening ability to cope with stress (10.2% vs. 20.7%). Overall, 84% reported being worried about future negative health effects of smoking, with no clear differences by quitting duration or vaping status. Conclusions: Most ex-smokers reported changes in their well-being since quitting, with more reporting improvements than declines. Well-being improved with duration of time since quitting, but did not appear to be influenced by daily vaping use, but stress coping may be better among vapers. Persisting worries about possible future health effects from smoking may be reducing the experienced benefits of quitting smoking for some.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".