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Record W4234278660 · doi:10.29392//001c.12008

Improving access to appropriate case management for common childhood illnesses in hard-to-reach areas of Abia State, Nigeria

2019· article· en· W4234278660 on OpenAlexfundno aff
Chinwoke Isiguzo, Samantha Herrera, Joy Ufere, Ugo Enebeli, Chukwuemeka Oluoha, Jennifer Anyanti, Debra Prosnitz

Bibliographic record

VenueJournal of Global Health Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
FundersGlobal Affairs CanadaWorld Health Organization
KeywordsAbiaState (computer science)BusinessEnvironmental healthMedicineGeographyComputer science

Abstract

fetched live from OpenAlex

Studies have demonstrated that trained community health workers can improve access to quality health services for under five children. Under the World Health Organization's Rapid Access Expansion Progamme, integrated community case management of childhood illnesses (iCCM) was introduced in Abia and Niger States, Nigeria in 2013. The objective of the program was to increase the number of children 2-59 months receiving quality life-saving treatment for malaria, pneumonia and diarrhoea by extending case management through community-oriented resource persons (CORPs). We present findings from household surveys comparing baseline and endline data to assess changes in sick child care-seeking, assessment, and treatment coverage provided over the project period in Abia State.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.363
Teacher spread0.346 · 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
Published2019
Admission routes1
Has abstractyes

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