Designing nutrition-sensitive agriculture (NSA) interventions with multi-criteria decision analysis (MCDA): a review
Bibliographic record
Abstract
Despite the efforts to end malnutrition through intensive agriculture of caloric crops, micronutrient deficiencies and other forms of malnutrition persist in vulnerable communities worldwide. Nutrition-sensitive agriculture (NSA) interventions are recognized as chances to address the causes of malnutrition. In this work, the different types of NSA interventions were explored, as well as the pathways through which they can improve nutrition (e.g., increasing biofortified crops and income generation via agricultural sales for a positive impact on access to nutritious foods, and simultaneously involving nutrition education to improve care practices and eventually nutritional status). Some NSA interventions focus on one pathway. Well-designed interventions, however, should follow multi-pathway approaches targeting the underlying causes of undernutrition within the selected population. The circumstances in which certain indicators should be used to measure the impact of an NSA intervention in each stage of the full pathway were also explained, as well as the need of enhancing the design of such interventions. Multi-criteria decision analysis (MCDA) has been employed to solve agriculture-related issues, but it has not been used to identify the optimal types of NSA interventions, metrics, and indicators based on the context of the community, priorities and objectives of the project managers and designers, etc.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".