Perceived Health and Capacity to Cope With Stress in Recent Ex-smokers: Impact of Vaping Versus Quitting All Nicotine
Bibliographic record
Abstract
INTRODUCTION: Little is known about the continued use of nicotine following smoking cessation on perceived well-being in comparison to complete cessation of nicotine use. AIMS AND METHODS: To explore aspects of perceived well-being and coping among recent ex-smokers as a function of vaping status. Ever-daily smokers in the International Tobacco Control 4 country smoking and vaping surveys in 2016 (w1 N = 883) and 2018 (w2 N = 1088). Cross-sectional associations and longitudinal samples for those who quit between waves and those who quit at w1 and maintained abstinence to w2. Main outcome measures were: Past 30 days of depression symptoms, perceived stress, stress management since quitting, and change in perceived day-to-day health. RESULTS: In the cross-sectional analyses vapers were more likely to report both improved stress management (aOR = 1.71, 95% CI 1.23-2.36) and perceived day-to-day health (aOR = 1.65, 95% CI 1.26-2.16) than nicotine abstainers. In the longitudinal analyses, smokers who switched to vaping between waves (n = 372) were more likely to report depression symptoms at w2 (aOR = 2.00, 95% CI 1.09-3.65) but reported improved perceived health (aOR = 1.92, 95% CI 1.16-3.20). For the past daily smokers who remained quit between waves (n = 382), vapers were more likely to report improved stress management relative to abstainers (RRR = 5.05. 95% CI 1.19-21.40). There were no other significant differences between vapers and nicotine abstainers. CONCLUSIONS: There is little evidence to support the view that perceptions of well-being deteriorate in vapers compared to complete nicotine abstainers in the immediate years after smoking cessation. IMPLICATIONS: This study could find no conclusive evidence that the continued use of nicotine via e-cigarettes was detrimental to health compared to completely stopping nicotine intake altogether. Our results would suggest that continuing to use nicotine may even result in some benefits in the short term such as improved stress management, however further longitudinal studies are required to examine if these effects are restricted to the early post-quitting phase and whether other positive or negative effects on psychosocial health emerge in the future.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".