The health, environmental, and cost implications of providing healthy and sustainable school meals for every child by 2030: a global modelling study
Bibliographic record
Abstract
BACKGROUND: School meal programmes are thought to improve dietary behaviour in children, with benefits sustained throughout the life course, making them important catalysts for wider food-system change. However, only one in five children globally currently receives school meals. We estimated the potential effects of extending school meal coverage to all children by 2030 for dietary health; the environmental effects related to diets; and the costs of diets at global, regional, and national levels. METHODS: We conducted health, environmental, and cost assessments of future scenarios of school meal coverage, meal frequency, meal composition, and food wastage. In the health assessment, we used statistical methods and a comparative risk assessment to estimate short-term changes in undernourishment and long-term changes in dietary risks and mortality. In the environmental assessment, we used food-related environmental footprints to analyse how changes in dietary composition and food waste affect greenhouse gas emissions, land use, and freshwater use. In the cost assessment, we used an international dataset of food prices to estimate changes in diet costs, and we used estimates of the social cost of carbon and the costs of illness to estimate changes in the costs of climate-change damages and in health-related costs. FINDINGS: Extending school meal programmes to all children globally by 2030 could be associated with substantial health and environmental benefits globally and in each country. In the model assessments, the prevalence of undernourishment in food-insecure populations was reduced by a quarter due to having an additional meal at school; more than 1 million cases of non-communicable diseases were prevented globally per year if dietary habits were partly sustained into adulthood; and food-related environmental effects were halved if meal composition adhered to recommendations for healthy and sustainable diets and food waste was reduced. Increasing school meal coverage incurred additional meal-related costs that ranged from 0·1% of gross domestic product (GDP) in high-income countries to 1·0% of GDP in low-income countries. Reductions in the external costs of climate-change damages and the costs of illness compensated for the costs of providing meals in line with health and sustainable diets. INTERPRETATION: Universal school meal coverage could make important contributions to improving children's health, the food security of their families, and the sustainability of food systems. However, dedicated policy and financial support will be required to close the gap in school meal coverage, especially in low-income countries. FUNDING: Research Consortium for School Health and Nutrition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".