What works for reducing stunting in low-income and middle-income countries? Cumulative learnings from the Global Stunting Exemplars Project
Bibliographic record
Abstract
BACKGROUND: Impaired linear growth and stunting in children under 5 y is a marker of multiple deprivations in low-income and middle-income countries. OBJECTIVES: We aimed to assess drivers and policies influencing improvements in linear growth and stunting reduction in 10 countries with annual rates of reduction in childhood stunting averaging 1.1% (range: 0.4%-1.7%) at national-level or subnational-level, and to improve a framework of action for other countries to follow. METHODS: We used mixed methods to assess trends and patterns of improvement in linear growth in children under 5 y using available household-level data and in-depth analysis of programs and their implementation. We assessed patterns of change with multivariate regression analyses of risk factors driving stunting and affecting change. We compared results from the Oaxaca-Blinder decomposition analyses using a hierarchical approach and retrospectively assessed the appropriateness of a previously proposed 10-step process for country-level planning and implementation processes. Limited data precluded robust serial assessment of dietary intake at individual level for children and mothers. RESULTS: Rapid reduction in childhood stunting is possible and findings across exemplar countries underscore the benefits of indirect and direct interventions in health and other social sectors. These include programs focusing on poverty alleviation; water, sanitation, and hygiene; promotion of girls' education and empowerment; and maternal nutrition. The potential benefits of family planning programs and factors contributing to gains in maternal nutrition were noted. In malarial endemic areas, malaria control programs were associated with improved childhood growth, and patterns of growth indicated continued benefits of childhood disease prevention and management strategies. CONCLUSIONS: A systematic, evidence-informed approach to improve maternal and child health and nutrition is feasible and, with targeting, can accelerate reduction in linear growth faltering in childhood.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".