Repeated training of accredited social health activists (ASHAs) for improved detection of visceral leishmaniasis cases in Bihar, India
Bibliographic record
Abstract
Accredited Social Health Activists (ASHAs) are incentive-based, female health workers responsible for a village of 1000 population and living in the same community and render valuable services towards maternal and child health care, polio elimination program and other health care-related activities including visceral leishmaniasis (VL). One of the major health concerns is that cases remain in the endemic villages for weeks without treatment causing increased likelihood to treatment failure and disease transmission in the community. To address this problem, we have begun a training program for ASHAs to enhance early detection of potential VL cases and referring them to their local Primary Health Centers (PHCs) for diagnosis and treatment. The result of this training showed increased referral rate to PHCs for diagnosis and treatment. Encouraged with the results from a single training session, we determined in the present study whether repeated training of ASHAs resulted in an a further increase in VL case referral to the local PHCs. After two training sessions, VL referrals by ASHAs increased to 46% as compared to 28% after a single training session in this cohort and a baseline of 7% before training. ASHA training is an effective way to conduct active case detection of VL cases and should be repeated once a year.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".