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Record W2114776210 · doi:10.4314/eamj.v83i11.9478

Nutritional status of food consumption patterns of young children living in Western Uganda

2007· article· en· W2114776210 on OpenAlexaff
Kim D. Raine, Andrea Bridge, Walter Kipp, Joseph Konde-Lule

Bibliographic record

VenueEast African Medical Journal · 2007
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineMalnutritionUnderweightEnvironmental healthPediatricsCross-sectional studyRural areaGerontologyDemographyBody mass indexOverweight

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this pilot study was to compare the nutritional status and food consumption patterns of children under five years. DESIGN: Quantitative, exploratory, cross sectional study. SETTING: Kabarole district, western Uganda. Kabarole district is a rural district with subsistence farming as the main income. SUBJECTS: Two hundred and five children between 12 and 72 months of age living in AIDS affected homes versus children living in non-AIDS affected homes were examined. RESULTS: Fifty-five percent of all children were stunted and 20.5% were underweight. There was no difference in the prevalence of malnutrition between children living in AIDS affected homes versus non-AIDS affected homes. Only children between 12-35 months suffered from a daily deficit in caloric intake. The older children consumed the basic recommended daily intake (RDI) for protein, fat, iron and vitamin A. Due to frequent disease episodes and limitations in the estimations of individual total energy expenditure, the results are likely underestimations of the children's true nutritional requirements. The type of foods given to children in AIDS affected homes and controls were quite similar. CONCLUSION: Young children in Kabarole district suffer from severe chronic malnutrition rates, but rates and feeding patterns are not different in AIDS affected versus non AIDS affected homes.

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 score0.004
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.275
Teacher spread0.259 · 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

Citations13
Published2007
Admission routes1
Has abstractyes

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