How Are Self-Reported Physical and Mental Health Conditions Related to Vaping Activities among Smokers and Quitters: Findings from the ITC Four Country Smoking and Vaping Wave 1 Survey
Bibliographic record
Abstract
This study examines whether having health conditions or concerns related to smoking is associated with use of vaping products. Data came from the 2016 wave of the International Tobacco Control Four Country Smoking and Vaping Survey. Smokers and recent quitters (n = 11,344) were asked whether they had a medical diagnosis for nine health conditions (i.e., depression, anxiety, alcohol problems, severe obesity, chronic pain, diabetes, heart disease, cancer, and chronic lung disease) and concerns about past and future health effects of smoking, and their vaping activities. Respondents with depression and alcohol problems were more likely to be current vapers both daily (Adjusted odds ratio, AOR = 1.42, 95% confidence interval, CI 1.09–1.85, p < 0.05 for depression; and AOR = 1.52, 95% CI 1.02–2.27, p < 0.05 for alcohol) and monthly (AOR = 1.32, 95% CI 1.11–1.57 for depression, p < 0.01; and AOR = 1.43, 95% CI 1.06–1.90, p < 0.05 for alcohol). Vaping was more likely at monthly level for those with severe obesity (AOR = 1.77, 95% CI 1.29–2.43, p < 0.001), cancer (AOR = 5.19, 95% CI 2.20–12.24, p < 0.001), and concerns about future effects of smoking (AOR = 1.83, 95% CI 1.47–2.28, p < 0.001). Positive associations were also found between chronic pain and concerns about past health effects of smoking and daily vaping. Only having heart disease was, in this case negatively, associated with use of vaping products on their last quit attempt (AOR = 0.72, 95% CI 0.43–0.91, p < 0.05). Self-reported health condition or reduced health associated with smoking is not systematically leading to increased vaping or increased likelihood of using vaping as a quitting strategy.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".