Non‐cigarette combustible tobacco use and its associations with subsequent cessation of smoking among daily cigarette smokers: findings from the International Tobacco Control Four Country Smoking and Vaping Surveys (2016–20)
Bibliographic record
Abstract
Abstract Aims To examine whether polyuse of cigarettes and other smoked products (polysmoking) is predictive of quit attempts and quit success. Design A prospective multi‐country cohort design. Setting Australia, Canada, England and the United States. Participants A total of 3983 adult daily cigarette smokers were surveyed in 2016 (wave 1 of data collection) and were re‐contacted in 2018 (wave 2) (i.e. waves 1–2 cohort) in the International Tobacco Control Four Country Smoking and Vaping (ITC 4CV) surveys; and 3736 smokers were surveyed in 2018 and re‐contacted in 2020 (wave 3) (i.e. waves 2–3 cohort). Measurements Participants were asked about their cigarette smoking and use of cigars, cigarillos, pipes and waterpipes. Outcomes were quit attempts between two survey waves and success, defined as having quit smoking all the combustible tobacco at the subsequent survey for 1 month or more. Findings Levels of polysmoking were 12.7% in the waves 1–2 cohort and 10.5% for the waves 2–3 cohort. Compared with cigarette‐only smokers, polysmokers were more likely to attempt to quit between waves 1 and 2 [54.9 versus 42.7%, adjusted odds ratio (aOR) = 1.37, 95% confidence interval (CI) = 1.08–1.74, P < 0.01], but not between waves 2 and 3 (43.8 versus 40.1%, aOR = 0.94, 95% CI = 0.72–1.22). Polysmoking predicted reduced likelihood of success in both cohorts among attempters and the overall samples. Between waves 2 and 3 there were significantly more transitions to non‐daily smoking among the polysmokers (12.4 versus 5.3%, χ2 = 40.4, P < 0.001). Conclusions There is a consistent association between polysmoking (use of cigarettes together with other smoked products) and reduced quit success for combustible tobacco, but it is probably due to increased likelihood of transitioning to non‐daily use rather than complete cessation.
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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 | 0.001 |
| 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".