Quit rates at 6 months in a pharmacist-led smoking cessation service in Malaysia
Bibliographic record
Abstract
BACKGROUND: Smoking cessation clinics have been established in Malaysia since 2004, but wide variations in success rates have been observed. This study aimed to evaluate the proposed pharmacist-led Integrated Quit Smoking Service (IQSS) in Sabah, Malaysia, and identify factors associated with successful smoking cessation. METHODS: Data from 176 participants were collected from one of the quit-smoking centres in Sabah, Malaysia. Pharmacists, doctors and nurses were involved throughout the study. Any health care provider can refer patients for smoking cessation, and free pharmacotherapy and counselling was provided during the cessation period for up to 3 months. Information on demographic characteristics, smoking behaviours, follow-up and pharmacotherapy were collected. The main outcome measure was the abstinence from smoking, which was verified through carbon monoxide in expired air during the 6-month follow-up. RESULTS: A 42.6% success rate was achieved in IQSS. Smoking behaviour such as lower cigarette intake and lower Fagerström score were identified as factors associated with success. On top of that, a longer duration of follow-up and more frequent visits were significantly associated with success in quitting smoking. CONCLUSION: Collaboration among health care practitioners should be the main focus, and we need a combination of proven effective modalities in order to create an ideal smoking cessation module.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".