How Can Nutrition Research Better Reflect the Relationship Between Wasting and Stunting in Children? Learnings from the Wasting and Stunting Project
Bibliographic record
Abstract
Childhood wasting and stunting affect large numbers of children globally. Both are important risk factors for illness and death yet, despite the fact that these conditions can share common risk factors and are often seen in the same child, they are commonly portrayed as relatively distinct manifestations of undernutrition. In 2014, the Wasting and Stunting project was launched by the Emergency Nutrition Network. Its aim was to better understand the complex relationship and associations between wasting and stunting and examine whether current separations that were apparent in approaches to policy, financing, and programs were justified or useful. Based on the project's work, this article aims to bring a wasting and stunting lens to how research is designed and financed in order for the nutrition community to better understand, prevent, and treat child undernutrition. Discussion of lessons learnt focuses on the synergy and temporal relationships between children's weight loss and linear growth faltering, the proximal and distal factors that drive diverse forms of undernutrition, and identifying and targeting people most at risk. Supporting progress in all these areas requires research collaborations across interest groups that highlight the value of research that moves beyond a focus on single forms of undernutrition, and ensures that there is equal attention given to wasting as to other forms of malnutrition, wherever it is present.
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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 | 0.149 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.013 |
| 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".