Impact of ASHA Training on Active Case Detection of Visceral Leishmaniasis in Bihar, India
Bibliographic record
Abstract
BACKGROUND: One of the major challenges for management of visceral leishmaniasis (VL) is early diagnosis of cases to improve treatment outcome and reduce transmission. We have therefore investigated active case detection of VL with the help of accredited social health activists (ASHA). ASHAs are women who live in the community and receive performance-based incentives for overseeing maternal and other health-related issues in their village. METHODS AND PRINCIPAL FINDING: Through conducting interviews with 400 randomly selected ASHAs from four primary health care centers (PHCs), it was observed that their level of knowledge about visceral leishmaniasis (VL) regarding transmission, diagnosis, and treatment was limited. The baseline data indicated that less than 10% of VL cases seeking treatment at the PHCs were referred by ASHAs. To increase the knowledge and the referral rate of VL cases by ASHAs, training sessions were carried out during the monthly ASHA meetings at their respective PHCs. Following a single training session, the referral rate increased from less than 10% to over 27% and the overall knowledge about VL substantially improved. It was not possible, however, to demonstrate that ASHA training reduced the time that individuals had fever before treatment at the PHC. CONCLUSIONS: Training ASHAs to identify VL cases in villages for early diagnosis and treatment at the local PHC is feasible and should be undertaken routinely to improve knowledge about VL.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".