The Development of Village Health Volunteers’ Competencies for Tuberculosis Care in Trang Province, Thailand
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) is a major public health concern resulting in high rates of morbidity and mortality worldwide, particularly in low- and middle-income countries, despite treatment having been available for over 50 years. It remains a crucial health problem in Thailand. This study aimed to develop a program for building tuberculosis (TB) care competencies of village health volunteers (VHVs) and to examine its effects on the outcomes of care for TB patients. METHODS: The competency development program (CDP) was developed based on the principles of empowerment and community-based TB care. Forty VHVs in two villages of Khoglor sub-district, Trang, Thailand were recruited. Participants were classified into control and experimental groups equally (n=20) by using a matched pair technique. RESULTS: The results showed that the mean scores of TB knowledge, attitude towards TB care, and TB care skills in the experimental group at the baseline and post-intervention were significantly different (p < 0.05). The mean scores of TB knowledge, attitude towards TB care, and TB care skills in the experimental and control groups at the baseline were not different. However, the scores of these three competencies at the post-intervention were significantly higher in the experimental group, compared to the control group (p < .05). CONCLUSION: Per the findings of the study, healthcare professionals should incorporate the principles of empowerment and community-based TB care in TB training programs in order to enhance TB care competencies of community health volunteers.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".