Food Systems Interventions for Nutrition: Lessons from 6 Program Evaluations in Africa and South Asia
Bibliographic record
Abstract
Although there is growing global momentum behind food systems strategies to improve planetary and human health-including nutrition-there is limited evidence of what types of food systems interventions work. Evaluating these types of interventions is challenging due to their complex and dynamic nature and lack of fit with standard evaluation methods. In this article, we draw on a portfolio of 6 evaluations of food systems interventions in Africa and South Asia that were intended to improve nutrition. We identify key methodological challenges and formulate recommendations to improve the quality of such studies. We highlight 5 challenges: a lack of evidence base to justify the intervention, the dynamic and multifaceted nature of the interventions, addressing attribution, collecting or accessing accurate and timely data, and defining and measuring appropriate outcomes. In addition to more specific guidance, we identify 6 cross-cutting recommendations, including a need to use multiple and diverse methods and flexible designs. We also note that these evaluation challenges present opportunities to develop new methods and highlight several specific needs in this space.
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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 | 0.066 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".