Integrating nutrition into the education sector in low‐ and middle‐income countries: A framework for a win–win collaboration
Bibliographic record
Abstract
Malnutrition-both undernutrition and overnutrition-is a public health concern worldwide and particularly in low- and middle-income countries (LMICs). The education sector has high potential to improve immediate nutrition outcomes by providing food in schools and to have more long-term impact through education. We developed a conceptual framework to show how the education sector can be leveraged for nutrition. We reviewed the literature to identify existing frameworks outlining how nutrition programs can be delivered by and through the education sector and used these to build a comprehensive framework. We first organized nutrition programs in the education sector into (1) school food, meals, and food environment; (2) nutrition and health education; (3) physical activity and education; (4) school health services; and (5) water, sanitation, and hygiene (WASH) sector. We then discuss how each one can be successfully implemented. We found high potential in improving nutrition standards and quality of school foods, meals and food environment, especially through collaboration with the agriculture sector. There is a need for well-integrated, culturally appropriate nutrition and health education into the existing school curriculum. This must be supported by a skilled workforce-including nutrition and public health professionals and school staff. Parental and community engagement is cornerstone for program sustainability and success. Current monitoring and evaluation of nutrition programming in schools is weak, and effectiveness, including cost-effectiveness, of interventions is not yet adequately quantified. Finally, we note that opportunities for leveraging the education sector in the fight against rising overweight and obesity rates are under-researched and likely underutilized in LMICs.
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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 | 0.108 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.037 | 0.030 |
| Open science | 0.006 | 0.052 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".