Social Ecological Factors Influencing Children's School Readiness in Low-Income South African Communities
Bibliographic record
Abstract
Research Findings: School readiness is highly salient in South Africa (SA), a country with extreme and persistent inequities that undermine early childhood development. The aim of this short-term longitudinal study was to identify social ecological factors associated with school readiness in young children from low-income settings in Cape Town, SA. Participants were 152 3–5-year-old children (55% female, not attending early childhood care and education (ECCE) settings at recruitment) and their primary adult caregiver from low-income settings. Linear regressions found that, compared to home- and community-level factors, child-level factors were the strongest predictors of scores on the International Development and Early Learning Assessment (IDELA, total and subscale scores for literacy, numeracy, social emotional, and motor). At the child level, attending ECCE services was the strongest predictor, followed by early numeracy and age. Household socioeconomic status positively predicted social emotional scores; dysfunction in the parent–child relationship negatively predicted literacy and total school readiness scores. Practice or Policy: These findings contribute to a contextually relevant understanding of school readiness in low-income SA settings. Greater understanding can lead to more effective mitigation of risks and amplification of protective factors within policy and practice so that early childhood development can be optimized in these settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".