Predictors of Successful Quitting among Thai Adult Smokers: Evidence from ITC-SEA (Thailand) Survey
Bibliographic record
Abstract
This study uses longitudinal data from the International Tobacco Control Southeast Asia (ITC-SEA Thailand) survey to explore patterns and predictors of successful quitting among Thai adult smokers as a function of time quit. A cohort of a representative sample of 2000 smokers was surveyed four times from 2005 to 2009. A sample of 1533 individuals provided data for at least one of the reported analyses. Over the four years of follow-up, 97% made attempts to quit. Outcomes were successful quitting/relapse: (a) quit attempts of at least one month (short-term relapse, 43%) (57% remaining quit); (b) surviving at least six months (medium-term) (31%); (c) relapse between one and six months (45%); (d) having continuously quit between Waves 3 and 4 (sustained abstinence) (14%); and (e) relapse from six months on (44%) compared to those who continuously quit between Waves 3 and 4 (56%). Predictors for early relapse (<1 month) differ from longer-term relapse. Age was associated with reduced relapse over all three periods, and was much stronger for longer periods of abstinence. Cigarette consumption predicted relapse for short and medium terms. Self-assessed addiction was predictive of early relapse, but reversed to predict abstinence beyond six months. Previous quit history of more than one week was predictive of early abstinence, but became unrelated subsequently. Self-efficacy was strongly predictive of abstinence in the first month but was associated with relapse thereafter. Some determinants of relapse change with time quit, but this may be in somewhat different to patterns found in the West.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".