Connecting the food and agriculture sector to nutrition interventions for improved health outcomes
Bibliographic record
Abstract
To achieve the Sustainable Development Goal of zero hunger, multi-sectoral strategies to improve nutrition are necessary. Building towards this goal, the food and agriculture sector must be considered when designing nutritional interventions. Nevertheless, most frameworks designed to guide nutritional interventions do not adequately capture opportunities for integrating nutrition interventions within the food and agriculture sector. This paper aims to highlight how deeply connected the food and agriculture sector is to underlying causes of malnutrition and identify opportunities to better integrate the food and agriculture sector and nutrition in low and middle income countries. In particular, this paper: (1) expands on the UNICEF conceptual framework for undernutrition to integrate the food and agriculture sector and nutrition outcomes, (2) identifies how nutritional outcomes and agriculture are linked in six important ways by defining evidence-based food and agriculture system components within these pathways: as a source of food, as a source of income, through food prices, women's empowerment, women's utilization of time, and women's health and nutritional status, and (3) shows that the food and agriculture sector facilitates interventions through production, processing and consumption, as well as through farmer practices and behavior. Current frameworks used to guide nutrition interventions are designed from a health sector paradigm, leaving agricultural aspects not sufficiently leveraged. This paper concludes by proposing intervention opportunities to rectify the missed opportunities generated by this approach. Program design should consider the ways that the food and agriculture sector is linked to other critical sectors to comprehensively address malnutrition. This framework is designed to help the user to begin to identify intervention sites that may be considered when planning and implementing multi-sectoral nutrition programs. Supplementary Information: The online version contains supplementary material available at 10.1007/s12571-022-01262-3.
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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 | 0.061 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".