Growth trajectories from conception through middle childhood and cognitive achievement at age 8 years: Evidence from four low- and middle-income countries
Bibliographic record
Abstract
Child chronic malnutrition is endemic in low- and middle-income countries and deleterious for child development. Studies investigating the relationship between nutrition at different periods of childhood, as measured by growth in these periods (growth trajectories), and cognitive development have produced mixed evidence. Although an explanation of this has been that different studies use different approaches to model growth trajectories, the differences across approaches are not well understood. Furthermore, little is known about the pathways linking growth trajectories and cognitive achievement. In this paper, we develop and estimate a general path model of the relationship between growth trajectories and cognitive achievement using data on four cohorts from Ethiopia, India, Peru, and Vietnam. The model is used to: a) compare two of the most common approaches of modelling growth trajectories in the literature, namely the lifecourse plot and the conditional body size model, and b) investigate the potential channels via which the association between growth in each period and cognitive achievement manifests. We show that the two approaches are expected to produce systematically different results that have distinct interpretations. Results suggest that growth from conception through age 1 year, between age 1 and 5 years, and between 5 and 8 years are each positively and significantly associated with cognitive achievement at age 8 years and that this may be partly explained by the fact that faster-growing children start school earlier. We also find that a significant share of the association between early growth and later cognitive achievement is mediated through growth in interim periods.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".