Collaborative Efforts in Smoking Cessation Clinics: An Evaluation of Healthcare Professionals' Contributions in Thailand
Bibliographic record
Abstract
Background: Thailand implemented the FAH-SAI Clinic smoking cessation service program in 2010, which provides services through a multidisciplinary team. However, the contribution of each healthcare professional in terms of activity frequency and time spent has not been formally assessed, and the appropriate incentive compensation needs to be evaluated. Method: We performed a prospective observational study, focusing on individuals aged 13 and above who were in the action stage. We utilized an activities-based approach and work points system to measure time contribution and calculate incentive compensation. Data were collected through a paper/electronic case record form and questionnaire. Descriptive statistics were used to report the outcomes. Results: Our study analyzed 2,041 participants and 4,098 visits, which reported 37,356 frequencies across 10 activities provided by healthcare professionals in smoking cessation clinics following the 5 As model. Nurses had the highest frequency of contributions (N of activity=23,979; 64.19%). Public health technical officers spent time the most with an average of 27.74 minutes. The top three professionals receiving incentive compensation per case were public health technical officers at 31.67 Baht, followed by nurses at 28.41 Baht, and physicians at 21.74 Baht. Conclusion: All healthcare professionals play important roles in smoking cessation service program with varying contributions based on time, frequency, and activities involved. To implement these findings, it is important to consider the performance of each setting and involve non-study stakeholders. Keywords: Smoking cessation clinic, Tobacco control, Smoking cessation, Thailand
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".