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Record W2996387122 · doi:10.9734/ejnfs/2015/20961

Lessons in Scale-up of a Public Sector Zinc ORS Intervention for Childhood Diarrhoea Management in the Indian State of Bihar

2015· article· en· W2996387122 on OpenAlexaff
Mahesh Srinivas, Sanjeev Kumar, Devaji Patil, Rakesh Jha, Rajiv Ranjan, Meena Jadhav

Bibliographic record

VenueEuropean Journal of Nutrition & Food Safety · 2015
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsNutrition International
Fundersnot available
KeywordsIntervention (counseling)State (computer science)Scale (ratio)ZincMedicinePublic sectorPolitical scienceGeographyNursingMathematicsMaterials scienceCartographyMetallurgy

Abstract

fetched live from OpenAlex

The Government of India introduced guidelines on therapeutic supplementation of zinc in the management of childhood diarrhoea in the year 2007, but programmatic constraints delayed its introduction into public health programs. Micronutrient Initiative, with support from a donor, initiated a program in 2010 to demonstrate and scale-up the use of zinc through public sector channels, in the state of Bihar. Methods: The project was implemented in 15 demonstration districts with a population of 36 million. Over 40,000 community level health workers were oriented on use of zinc. Support was provided to strengthen procurement and supply chain mechanisms. A robust system of monitoring and evaluation was introduced to track performance. Meaningful engagement with the government ensued throughout the demonstration phase. Results: Use of Community-level volunteers (CLVs) is a pre-requisite to scaling-up access to care. More than one-million children were reported to be provided care. The CLVs need to be engaged through a relevant supportive supervision model. Supply chain mechanisms need to be strengthened to prevent stock-outs at service delivery points. Simple reporting tools need to be introduced for improved case-reporting. Conclusions: It is feasible and viable to introduce and scale-up therapeutic zinc supplementation Conference

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)0.003
metaresearch head score (Gemma)0.000
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 score0.792
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.050
GPT teacher head0.288
Teacher spread0.238 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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