Rethinking policy perspectives on childhood stunting: time to formulate a structural and multifactorial strategy
Bibliographic record
Abstract
Stunting and chronic undernutrition among children in South Asia remain a major unresolved global health issue. There are compelling intrinsic and moral reasons to ensure that children attain their optimal growth potential facilitated via promotion of healthy living conditions. Investments in efforts to ensure that children's growth is not faltered also have substantial instrumental benefits in terms of cognitive and economic development. Using the case of India, we critique three prevailing approaches to reducing undernutrition among children: an over-reliance on macroeconomic growth as a potent policy instrument, a disproportionate focus on interpreting undernutrition as a demand-side problem and an over-reliance on unintegrated single-factorial (one at a time) approaches to policy and research. Using existing evidence, we develop a case for support-led policy approach with a focus on integrated and structural factors to addressing the problem of undernutrition among children in India. Key messages Eliminating child undernutrition is important from an intrinsic perspective and offers considerable instrumental benefits to individual and society. Evidence suggests that an exclusive reliance on a growth-mediated strategy to eliminate stunting needs to be reconsidered, suggesting the need for a substantial support-led strategy. Interpreting and addressing undernutrition as a demand-side problem with proximal single-factorial interventions is futile. There is an urgent need to develop interventions that address the broader structural and upstream causes of child undernutrition.
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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 | 0.033 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.014 | 0.027 |
| 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".