A trial to mobilize NGO health volunteers to improve tuberculosis patient care in Sana'a City, Yemen.
Bibliographic record
Abstract
OBJECTIVES: The study aims to show the feasibility of involvement of Non-Governmental Organization (NGO) health volunteers with regular monitoring mechanism on tuberculosis (TB) control in Sana'a City, Yemen. METHODS: Interventions to mobilize NGO health volunteers with regular monitoring field visits in two selected districts with approximately 400,000 population in Sana'a City were conducted. 52 NGO health volunteers who belonged to a domestic NGO were trained on TB case finding and case holding activities by the national TB control programme staff during the fourth quarter of 2004. RESULTS: 136 new smear-positive TB cases were enrolled from January 2005 to September 2006. The cure rates indicated significant improvement from 73.4% to 84.6% after start of the intervention (p = 0.023). The cure rate of patients whose treatment partners were health volunteers was significantly higher than patients whose treatment partners were health centre staff (93.3% vs. 79.8%, Exact p = 0.045). CONCLUSION: The present study showed the favourable results of the implementation of the intervention in two selected districts in Sana'a City with regards to the treatment outcomes. The National Tuberculosis Control Programme has decided to expand the NGO's health volunteers' involvement as treatment partners to at least urban settings in Yemen.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".