The Potential Contribution of the Health System to Reducing Stunting in SUN Countries
Bibliographic record
Abstract
BACKGROUND: The global nutrition community has called for a multisectoral approach to improve nutritional outcomes. While most essential nutrition interventions are delivered through the health system, nutrition-sensitive interventions from other sectors are critical. OBJECTIVE: We modeled the potential impact that Scaling Up Nutrition (SUN) interventions delivered by the health system would have on reaching World Health Assembly (WHA) stunting targets. We also included results for targets 2, 3, and 5. METHODS: Using all available countries enrolled in the SUN movement, we identified nutrition interventions that are delivered by the health system available in the Lives Saved Tool. We then scaled these interventions linearly from 2012 up to nearly universal coverage (90%) in 2025 and estimated the potential impact that this increase would have with regard to the WHA targets. RESULTS: Our results show that only 16 countries out of 56 would reach the 40% reduction in the number of stunted children by 2025, with a combined total reduction of 32% across all countries. Similarly, only 2 countries would achieve the 50% reduction in anemia for women of reproductive age, 41 countries would reach at least 50% exclusive breastfeeding in children under 6 months of age, and 0 countries would reach the 30% reduction in low birth weight. CONCLUSIONS: While the health system has an important role to play in the delivery of health interventions, focusing investments and efforts on the health system alone will not allow countries to reach the WHA targets by 2025. Concerted efforts across multiple sectors are necessary.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".