Assessment of the real-world impact of the Thai smoking cessation programme on clinical outcomes: protocol for a multicentre prospective observational study
Bibliographic record
Abstract
BACKGROUND: Tobacco smoking is the most common preventable cause of morbidity and mortality in the world. In an effort to counteract the harmful consequences of smoking, various tobacco control measures have been implemented, including the use of smoking cessation programmes to reduce the number of new smokers as well as helping current smokers to quit smoking. In Thailand, the SMART Quit Clinic Program (FAH-SAI Clinics) was launched in 2010 to provide smoking cessation services by a multidisciplinary team. There are currently 552 FAH-SAI Clinics established across all 77 provinces of Thailand. AIM: This protocol describes a study aiming to evaluate the SMART Quit Clinic Program (FAH-SAI Clinics) in terms of programme performance and clinical outcomes. We hope that the results of the study could be used to improve the current service model and the programme's success. METHOD: A multicentre prospective observational study will be conducted. The study will focus on 24 FAH-SAI Clinics across 21 provinces of Thailand. The primary outcomes are seven-day point prevalence abstinence rate and continuous abstinence rate at three and six months. The outcomes will be measured using a self-reported questionnaire and biochemical validated by exhaled carbon monoxide. DISCUSSION: This study will be the first real-world study that reports the effectiveness of the well-established smoking cessation programme in Thailand. Findings from this study can help improve the quality of smoking cessation services provided by multidisciplinary teams and other smoking cessation services, especially those implemented in low- and middle-income countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".