Age-Related Interactions on Key Theoretical Determinants of Smoking Cessation: Findings from the ITC Four Country Smoking and Vaping Surveys (2016–2020)
Bibliographic record
Abstract
BACKGROUND: This paper explores whether plans to quit, wanting to quit, and quit efficacy add predictive value over measures of habit strength and dependence in making quit attempts and/or attaining smoking abstinence. AIMS AND METHODS: We used three waves of the International Tobacco Control (ITC) Four Country Smoking and Vaping Survey conducted in 2016, 2018, and 2020. Baseline daily smokers (N = 6710) who provided data for at least one wave-to-wave transition (W1 to W2, N = 3511 or W2 to W3, N = 3199) and provided outcome data at the next wave (follow-up) formed the analytic sample. Generalized estimating equations (GEE) logistic regression analyses examined predictors of quit attempts and abstinence at follow-up (1- and 6-month sustained abstinence). RESULTS: Wanting and planning to quit were significantly positively associated with making quit attempts, but negatively associated with smoking abstinence. A significant interaction between the Heaviness of Smoking Index and age warranted an age-stratified analysis for both abstinence outcomes. Lower HSI predicted abstinence in only the younger smokers Motivation and plans to quit were positively associated with abstinence in younger smokers, but surprisingly were negatively associated with abstinence in older smokers. Quit efficacy was associated with abstinence in the older, but not the younger smokers. CONCLUSIONS: Models of smoking abstinence are significantly improved by including motivational predictors of smoking. Age was an important moderator of the association between abstinence for both dependence and motivational variables. IMPLICATIONS: The findings from this large cohort study indicate there are age-related differences in predictors of smoking abstinence but not quit attempts. These associations may reflect differential experiences of older and younger cohorts of smokers, which may have implications for interventions to motivate and assist smokers in quitting.
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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 | 0.006 | 0.013 |
| 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 | 0.000 | 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".