School Feeding and the Challenge of Supporting Nutritional Needs of Pupils in Ghana
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".