Home learning environment and physical development impact children’s executive function development and literacy in rural Côte d’Ivoire
Bibliographic record
Abstract
Socio-economic status (SES) is closely linked to children’s reading development. Previous research suggests that executive functions (EF) mediate the effects of SES on reading, however, this research has almost exclusively focused on high-income countries (HICs). Comparatively less is known about the mechanisms that link SES and literacy in low-and-middle-income countries (LMICs). Childhood experiences of poverty in LMICs have been consistently linked to cognitive development through two sets of predictors: nutrition and physical growth, and the availability of educational scaffolding at home.The influence of the home learning environment (i.e. material deprivation, types of caregiver interactions) and nutrition to support children’s physical development (i.e. children’s BMI and stature for their age) on EF and literacy was examined in 630 primary-school children (6-14 years) in rural Côte d'Ivoire, West Africa. Structural equation modeling revealed that SES had an indirect effect on EF via the home learning environment, and in turn, reading. Importantly, the home learning environment, and a child’s physical development and nutrition showed distinct contributions to EF. The results suggest that improved educational scaffolding at home and supplemented nutrition could support EF development and reduce the negative impact of socioeconomic risk factors on reading.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".