MétaCan
Menu
Back to cohort
Record W2587507839 · doi:10.5539/jfr.v6n2p11

Nutrition Intakes and Nutritional Status of School Age Children in Ghana

2017· article· en· W2587507839 on OpenAlexvenueno aff
Justina Serwaah Owusu, Esi K. Colecraft, Richmond NO Aryeetey, Joan A. Vaccaro, Fatma G. Huffman

Bibliographic record

VenueJournal of Food Research · 2017
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
Fundersnot available
KeywordsUnderweightMalnutritionEnvironmental healthMicronutrientLogistic regressionMedicineCross-sectional studyPediatricsObesityOverweight

Abstract

fetched live from OpenAlex

This paper compares nutrition intakes and nutritional status of school children from two public schools in neighbouring communities of Ghana with different school feeding programmes. One hundred and eighty-two caregiver and school-age child pairs were interviewed concerning socio-demographics, dietary practices, and food security in a cross-sectional design. The independent t-test was used to compare the contribution of the publicly funded Ghana School Feeding Programme and private School Feeding Programme meals to total daily nutrient intakes of the children. Predictors of nutritional status of the children were assessed using logistic regression models. The private school feeding programme contributed more energy, protein, and micronutrients as compared to the government school feeding programme. About two-thirds (67.0%) of the children were stunted, underweight, or anaemic. Child’s age was a significant predictor of stunting. Undernutrition was prevalent among children from both programmes. Improved quality of diet from the feeding programmes may contribute to addressing malnutrition in these children.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.401
Teacher spread0.309 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Food ResearchSame topicChild Nutrition and Water AccessFrench-language works237,207