Motivational factors predict quit attempts but not maintenance of smoking cessation: Findings from the International Tobacco Control Four country project
Bibliographic record
Abstract
AIM: To explore whether measures of motivation to quit smoking have different predictive relationships with making quit attempts and the maintenance of those attempts. METHODS: Data are from three wave-to-wave transitions of the International Tobacco Control Four (ITC-4) country project. Smokers' responses at one wave were used to predict the likelihood of making an attempt and among those trying the likelihood of maintaining an attempt for at least a month at the next wave. For both outcomes, hierarchical logistic regressions were used to explore the predictive capacity of seven measures of motivation to quit smoking, controlling for a range of other known or possible predictors. RESULTS: Bivariate analyses indicate that measures of motivation to quit are predictive of making quit attempts, but they predict relapse among those making attempts. Multivariate analyses identified wanting to quit and frequency of prematurely butting out cigarettes as the main positive predictors of making attempts, but this was reduced by intention and recency of last attempt. For maintenance, premature butting out was the main motivation variable predicting relapse and was essentially unaffected by other measures. DISCUSSION: The findings show that it is wrong to suggest that all one needs to quit is to be motivated to do so. The reality is that one needs to be motivated to prompt action to stop smoking, but this is not sufficient in and of itself to ensure that cessation is maintained. These findings call attention to the importance of understanding the differential roles that prequit and postquit experiences play in smoking cessation and of providing help to smokers to stay off cigarettes.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".