Strategies to reduce malnutrition in children: what works in low-resource settings?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Child malnutrition in low-and-middle-income countries remains persistently high, driven by converging biological, social, economic, environmental, and conflict-related factors. As progress slows and vulnerabilities intensify, this review synthesises emerging evidence from recent years to identify effective strategies and future directions for reducing undernutrition in resource-constrained settings. RECENT FINDINGS: Recent literature demonstrates that nutrition-sensitive interventions, including women's empowerment, social protection, WASH, immunisation, kitchen gardens, and biofortification, address key underlying drivers of child malnutrition and contribute to improved growth and dietary diversity. Building on these foundations, nutrition-specific strategies such as antenatal micronutrient supplementation, optimal infant and young child feeding practices, fortified complementary foods, and emerging approaches like microbiota-directed foods and fermentation have shown measurable gains in growth and nutritional status. Across the evidence base, integrated and multisectoral delivery models consistently outperform standalone programs, with particularly strong results when nutrition is combined with health services, social protection, community-based platforms, or climate- and conflict-responsive strategies. SUMMARY: Current evidence underscores a shift toward integrated, layered, and context-responsive programming as the most effective path to reducing child malnutrition. Future research should prioritise implementation models that bridge nutrition-specific and nutrition-sensitive domains, strengthen health and community systems, and adapt to climate and humanitarian pressures.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".