Differences in Smoking Cessation Behaviors and Vaping Status among Adult Daily Smokers with and Without Depression, Anxiety, and Alcohol Use: Findings from the 2018 and 2020 International Tobacco Control Four Country Smoking and Vaping (ITC 4CV) Surveys
Bibliographic record
Abstract
This study examined differences in quit attempts, 1-month quit success, and vaping status at follow-up among a cohort of 3709 daily smokers with and without depression, anxiety, and regular alcohol use who participated in both the 2018 and 2020 International Tobacco Control Four Country Smoking and Vaping (ITC 4CV) Surveys. At baseline, a survey with validated screening tools was used to classify respondents as having no, or one or more of the following: 1) depression, 2) anxiety, and 3) regular alcohol use. Multivariable adjusted regression analyses were used to examine whether baseline (2018) self-report conditions were associated with quit attempts; quit success; and vaping status by follow-up (2020). Results showed that respondents who reported depressive symptoms were more likely than those without to have made a quit attempt (aOR = 1.32, 95% CI:1.03–1.70, p = 0.03), but were less likely to have quit (aOR = 0.55, 95% CI:0.34–0.89, p = 0.01). There were no differences in quit attempts or quit success between those with and without self-reported anxiety diagnoses or regular alcohol use. Among successful quitters, respondents with baseline depressive symptoms and self-reported anxiety diagnoses were more likely than those without to report vaping at follow-up (aOR = 2.58, 95% CI:1.16–5.74, p = 0.02, and aOR = 3.35 95% CI:1.14–9.87, p = 0.03). In summary, it appears that smokers with depression are motivated to quit smoking but were less likely to manage to stay quit, and more likely to be vaping if successfully quit. As smoking rates are higher among people with mental health conditions, it is crucial for healthcare professionals to identify these vulnerable groups and offer tailored smoking cessation support and continued support during their quit attempt.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".