Nutritional quality and diversity in Ghana’s school feeding programme: a mixed-methods exploration through caterer interviews in the Greater Accra Region
Bibliographic record
Abstract
BACKGROUND: The Ghana School Feeding Programme (GSFP) provides public primary school pupils with a free daily meal. Each meal is expected to follow set menus, providing 30% of children's' (6-12 years) energy requirements. This study assessed the nutritional quality and diversity of planned and provided GSFP meals, engaging school caterers to identify how meal quality in the Greater Accra Region could be enhanced. METHODS: A cross-sectional mixed methods study design was used. Multistage sampling was used to select 129 schools implementing the GSFP in six districts of the Greater Accra Region. GSFP district menus were collected as well as a one-week school caterer recall of provided school meals. The meal served on the day of data collection was recorded and photographed. Nutritional quality was evaluated based on nutrient profiling methods: energy density (low<125kcal/100g; medium 125-225kcal/100g; high>225kcal/100g) and nutrient density (low<5%; medium 5-10%; high>10%). Meal diversity was assessed by a simple count composed of 5 food groups: cereals, pulses/nuts/seeds, animal-source, vegetables and fruits. Caterers' views on programme facilitators and barriers were also explored. RESULTS: Planned menus included 14-20 weekly options, composed of eight minimally processed traditional dishes. All meals, except white rice, had a high nutrient density/100g. Energy density was varied (low, n=2; medium, n=2; high, n=4). Meals included only 2/5 or 3/5 food groups, mainly starchy staples, pulses/nuts/seeds, and sometimes vegetables. Fruit was never reported. About half of caterers (51.1%) reported deviating from the planned menus: 11.7% served alternative meals, with some including animal-sourced food (17.0%), and 39.4% repeated meals provided during the week, often based on starchy staples, influencing overall nutritional quality. Most caterers reported food item cost and lack of food purchase guidelines as barriers to providing school meals, while food safety training and guidelines for food preparation were facilitators. CONCLUSIONS: While school meals are composed of minimally processed, nutrient dense, local foods, there are notable gaps in meal diversity and compliance, as reflected in provided meals. Caterer compliance to planned menus varied greatly, reflecting recent food price inflation. Upwardly adjusting the current meal allocation of 1.2 cedis (0.22USD) per child per day could enhance access to more affordable, nutritious and diverse foods in school meals.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".