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Record W4211183743 · doi:10.33425/2641-4295.1037

School Feeding and the Challenge of Supporting Nutritional Needs of Pupils in Ghana

2021· article· en· W4211183743 on OpenAlexaff
Mabel Kyei Kwofi

Bibliographic record

VenueFood Science & Nutrition Research · 2021
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcGill University
Fundersnot available
KeywordsAttendanceMalnutritionSchool mealPopulationEnvironmental healthMealPsychologyMedicineGeographyPolitical science

Abstract

fetched live from OpenAlex

The Ghana free school feeding program was implemented to diminish short-term hunger and malnutrition among elementary school children, to increase school enrolment, attendance, and retention in primary schools and to boost domestic food production through patronizing local agricultural food production by small-scale farmers. This research sought to examine the nutritional diversity of the school-served meals and their ability to support the nutritional needs of the pupils. The research study was carried out at the New Juaben Municipality, Koforidua, Ghana. Four deprived elementary schools enrolled in the school feeding program in the New Juaben North District in Ghana were selected; the population consisted of pupils who were between the ages of 6 to 15 years. The survey data were collected through interviews and personal observations. The data on the nutritional diversity of foods were determined through the menu and food ingredients used in each meal preparation, these were compared with the standard nutritional composition. To evaluate the established objectives on pupils’ nutrition support from the school feeding program, tables were designed, with individual menus from each school, food groups, and the assessment of nutrients comprised in each local food item. The study outcome revealed the school’s cyclical menus had selected foods stuff that comprised of all the nutrients needed for a healthy life to support school children’s nutritional needs.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.079
GPT teacher head0.385
Teacher spread0.306 · 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 designBench or experimental
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

Citations5
Published2021
Admission routes1
Has abstractyes

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