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Record W2315767706 · doi:10.1080/20477724.2016.1156902

Repeated training of accredited social health activists (ASHAs) for improved detection of visceral leishmaniasis cases in Bihar, India

2016· article· en· W2315767706 on OpenAlexafffund
Vidya Nand Ravi Das, Ravindra Nath Pandey, Vijay Kumar, Krishna Pandey, Niyamat Ali Siddiqui, Rakesh Bihari Verma, Greg Matlashewski, Pradeep Das

Bibliographic record

VenuePathogens and Global Health · 2016
Typearticle
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsMcGill University
FundersGrand Challenges Canada
KeywordsAshaMedicineReferralAccreditationHealth careFamily medicineSocial determinants of healthPopulationPhysical therapyNursingEnvironmental healthPublic healthMedical education

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.379
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2016
Admission routes2
Has abstractyes

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