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Record W2167969676

Implementation of a National Integrated Management of Childhood Illness (IMCI) Program in Uganda

2004· article· en· W2167969676 on OpenAlexvenueno aff
Jesca Nsungwa‐Sabiiti, Gilbert Burnham, George Pariyo

Bibliographic record

VenueWorld health & population · 2004
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsIntegrated Management of Childhood IllnessMedicineReferralHealth facilityNursingProgram evaluationDeveloping countryPublic healthPopulationEnvironmental healthHealth servicesEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

Summary Uganda was among the first countries to implement the Integrated Management of Childhood Illness (IMCI) approach on a national scale, beginning in 1995. The program benefited from strong child health structures in the Ministry of Health, and generous support of donors. This enabled the training of over 8000 health workers in IMCI methods and to begin pre-service training and training for private practitioners. Training was decentralized to district level in 2000, and a short training course was developed in 2002. When the results of the national program were examined in 10 districts, the presence of IMCI-trained health workers in health centers was patchy. Supervisors observed health workers using their IMCI skills at only about half of visits. However, turnover of health staff following IMCI training was generally low. Low utilization of health facilities has reduced the potential benefit of the IMCI program. The pressure for rapid implementation of IMCI resulted in neglecting development of strong monitoring methods, a consistent supervisions system, and methods to assess the quality of IMCI training. The need for a hospital referral component was not appreciated until well into implementation. Although the need for community IMCI was recognized early in Uganda, development of the core components and the implementation process required much longer than anticipated.

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)0.000
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 score0.173
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.373
Teacher spread0.356 · 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

Citations17
Published2004
Admission routes1
Has abstractyes

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