Growth recovery and faltering through early adolescence in low- and middle-income countries: Determinants and implications for cognitive development
Bibliographic record
Abstract
Child chronic undernutrition, as measured by stunting, is prevalent in low- and middle-income countries and is among the major threats to child development. While stunting and its implications for cognitive development have been considered irreversible beyond early childhood there is a lack of consensus in the literature on this, as there is some evidence of recovery from stunting and that this recovery may be associated with improvements in cognition. Less is known however, about the drivers of growth recovery and the aspects of recovery linked to cognitive development. In this paper we investigate the factors associated with growth recovery and faltering through age 12 years and the implications of the incidence, timing, and persistence of post-infancy recovery from stunting for cognitive development using longitudinal data from Ethiopia, India, Peru, and Vietnam. We find that the factors most systematically associated with accelerated growth both before and after early childhood and across countries include mother's height, household living standards and shocks, community wages, food prices, and garbage collection. Our results suggest that post-infancy recovery from stunting is more likely to be systematically associated with higher achievement scores across countries when it is persistent and that associations between growth trajectories and cognitive achievement in middle childhood do not persist through early adolescence across countries. Overall, our findings indicate that growth after early childhood is responsive to changes in the household and community environments and that growth promotion after early childhood may yield improvements in child cognitive development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".