Methods and factors influencing successful smoking cessation in Thailand: A case-control study among smokers at the community level
Bibliographic record
Abstract
INTRODUCTION: Despite comprehensive tobacco control policies being in place since 1992, smoking prevalence in Thailand has not declined since 2009, indicating a potential need for individual-level measures. This study examined factors influencing successful smoking cessation attempts in Thailand. METHODS: With a case-control design, smoking cessation experiences of 284 successful (defined as having quit smoking for at least six months) and 837 unsuccessful quitters, who were all lifetime daily smokers, were compared, using sociodemographic data, smoking behaviors, and smoking cessation experiences from their last quitting attempt. Data were collected between August and December 2020. Multivariate-adjusted logistic regressions were employed. RESULTS: Unaided smoking cessation was the most popular method among Thais attempting to quit smoking; more than 99% of both successful and unsuccessful quitters used this method. A significantly higher proportion of successful quitters favored stopping their smoking abruptly than did unsuccessful quitters. Depending on the cessation phases (nicotine withdrawal or relapse prevention), cessation-supporting factors included a doctor's recommendation to stop smoking due to smoker's sickness (OR=2.6; 95% CI: 1.9-3.6), having a grandchild (OR=2.5; 95% CI: 1.1-5.6) or child (OR=2.0; 95% CI: 1.2-3.1), exercising (OR=13.9; 95% CI: 7.2-26.9), avoiding smokers (OR=6.7; 95% CI: 4.1-11.1), self-efficacy (OR=8.5; 95% CI: 3.6-20.0), having a good appetite (OR=1.9; 95% CI: 1.3-2.8), wishing to avoid the unpleasant smell of other people's smoking after cessation (OR=3.7; 95% CI: 2.5-5.5), smoking prohibitions in public places (OR=2.8; 95% CI: 1.2-6.4) and workplaces (OR=4.5; 95% CI: 1.9-10.3), and expensive tobacco (OR=1.9; 95% CI: 1.3-2.9). Barriers to successful cessation included using roll-your-own (OR=0.4; 95% CI: 0.3-0.5), insomnia (OR=0.3; 95% CI: 0.2-0.5), social pressure to smoke (OR=0.4; 95% CI: 0.3-0.6), associating smoking with a habit/specific activity (OR=0.4; 95% CI: 0.3-0.5), and pleasure of smoking (OR=0.5; 95% CI: 0.3-0.7). CONCLUSIONS: This study highlights several factors found to influence successful smoking cessation among Thai smokers which can be used to design a guideline for unaided smoking cessation, and for smoking cessation enhancement programs and policies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".