Executive functioning skills and their environmental predictors among pre‐school aged children in South Africa and The Gambia
Bibliographic record
Abstract
Executive functions (EFs) in early childhood are predictors of later developmental outcomes and school readiness. Much of the research on EFs and their psychosocial correlates has been conducted in high-income, minority world countries, which represent a small and biased portion of children globally. The aim of this study is to examine EFs among children aged 3-5 years in two African countries, South Africa (SA) and The Gambia (GM), and to explore shared and distinct predictors of EFs in these settings. The SA sample (N = 243, 51.9% female) was recruited from low-income communities within the Cape Town Metropolitan area. In GM, participants (N = 171, 49.7% female) were recruited from the rural West Kiang region. EFs, working memory (WM), inhibitory control (IC) and cognitive flexibility (CF), were measured using tablet-based tasks. Associations between EF task performance and indicators of socioeconomic status (household assets, caregiver education) and family enrichment factors (enrichment activities, diversity of caregivers) were assessed. Participants in SA scored higher on all EF tasks, but children in both sites predominantly scored within the expected range for their age. There were no associations between EFs and household or familial variables in SA, except for a trend-level association between caregiver education and CF. Patterns were similar in GM, where there was a trend-level association between WM and enrichment activities but no other relationships. We challenge the postulation that children in low-income settings have poorer EFs, simply due to lower socioeconomic status, but highlight the need to identify predictors of EFs in diverse, global settings. RESEARCH HIGHLIGHTS: Assessed Executive Functioning (EF) skills and their psychosocial predictors among pre-school aged children (aged 3-5 years) in two African settings (The Gambia and South Africa). On average, children within each setting performed within the expected range for their age, although children in South Africa had higher scores across tasks. There was little evidence of any association between socioeconomic variables and EFs in either site. Enrichment activities were marginally associated with better working memory in The Gambia, and caregiver education with cognitive flexibility in South Africa, both associations were trend-level significance.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".