Caregiver perspectives of risk and protective factors influencing early childhood development in low-income, urban settings: a social ecological perspective
Bibliographic record
Abstract
South Africa is a diverse country characterised by stark inequalities that undermine early childhood development (ECD). Caregivers of young children play a critical role in providing nurturing care to mitigate against risks to ECD. The aim of this qualitative study was to explore caregivers’ perceptions of factors influencing ECD in a low-income, urban South African setting, from a social ecological perspective. Individual interviews were conducted with 15 caregivers of 3-5-year-old children from three low-income communities in Cape Town, and a reflexive thematic analysis approach was adopted. In the family and home context, caregivers spoke about their role in developing, nurturing, providing, protecting, and disciplining their children. Risks in this context included low socioeconomic status, dysfunctional relationships, and caregiver mental health; resources related to early learning and social support. In the preschool / school context, caregivers discussed the value of early learning, and priorities for selecting early childhood care and education settings. In the community context, risks included violence and crime, whereas resources mentioned were social support, community programmes, and infrastructure. Caregivers also shared perceptions of the community’s role in their child’s development. These findings should be considered in light of the structural violence experienced in low-income settings in South Africa in order to holistically address risks and amplify protective factors for promoting ECD and the provision of nurturing care 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".