Future nicotine use preferences of current cigarette smokers: Findings from the 2020 International Tobacco Control Four Country Smoking and Vaping Survey
Bibliographic record
Abstract
INTRODUCTION: Consumer preferences should be important factors that are considered when developing health policies and interventions. This paper examines the prevalence of, and factors associated with, consumer preferences regarding smoking behaviour 1 to 2 years in the future. METHODS: At least weekly cigarette smokers in the 2020 wave of the International Tobacco Control Four Country Smoking and Vaping Survey (USA, Canada, England and Australia) (N = 8642) were asked if they preferred to continue to smoke or to quit with or without an alternative nicotine product (ANP) over the next 1-2 years. RESULTS: Country-specific weighted data showed 21.5% preferred to continue smoking and 8.0% were uncertain, leaving 70.6% preferring to quit: 13.7% using an ANP and 56.9% completely quitting nicotine. Apart from interest in quitting, the main predictors of preferring to quit were history of vaping, being aged 55 and over, smoking weekly, worrying about smoking harms, regretting starting and believing vaping is less harmful relative to smoking. Among those preferring to quit, preferring to use ANPs in future was very strongly associated with current vaping (especially daily), being younger, living in England, reporting strong urges to smoke, believing vaping is much less harmful than smoking, and not strongly regretting starting to smoke, and not wanting to quit. DISCUSSION AND CONCLUSIONS: A significant minority of smokers preferred not to quit, at least in the next year or two. Both interest in quitting and preference for ANPs over complete cessation were associated with similar covariates, including interest in vaping.
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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 | 0.001 | 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".