Smoking-related thoughts and microbehaviours, and their predictive power for quitting
Bibliographic record
Abstract
BACKGROUND: Negative attitudes to smoking are well-established predictors of intentions to quit and quit behaviours, but less attention has been given to whether quitting is influenced by smoking-related thoughts and microbehaviours that reflect a concern about smoking. OBJECTIVES: This paper aimed to describe the occurrence of smoking-related thoughts and microbehaviours among Chinese smokers, and to examine their predictive power for making quit attempts and sustained abstinence. METHODS: The data came from the first three waves of the International Tobacco Control China Survey. Four measures of recent thoughts about smoking and two microbehaviour measures (collectively referred to as microindicators) were examined. RESULTS: Most smokers (around three-quarters) reported thinking about harms of smoking to themselves or to others at least occasionally, and an increasing minority reported the two microbehaviours of prematurely butting out cigarettes and forgoing them. All microindicators were positively related to subsequent quit attempts in individual predictor analyses, but only serious thoughts about quitting and butting out cigarettes had independent relationships. Overall, there was no clear relationship between these microindicators and sustained abstinence. CONCLUSIONS: There was a moderately high level of occurrence of recent smoking-related thoughts and microbehaviours among the Chinese adult smokers in the six cities studied. Like in the West, microindicators of concern about smoking were positively associated with subsequent quit attempts, but unlike in the West, they were largely unrelated to sustained abstinence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".