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Record W4408309654 · doi:10.1155/cad/1870996

“Love, You Need to Give Your Child Love”: Mothers’ Perceptions of Nurturing Care for Young Children in South Africa

2025· article· en· W4408309654 on OpenAlexfundno aff
Wiedaad Slemming, Emmanuel Cohen, Alessandra Prioreschi, Stephanie V. Wrottesley, Shane A. Norris

Bibliographic record

VenueNew Directions for Child and Adolescent Development · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
FundersMedical Research CouncilCanadian Institutes of Health ResearchSouth African Medical Research CouncilNational Research Foundation
KeywordsPsychologyDevelopmental psychologyPerceptionChild careChild developmentChild rearingEarly childhood educationSocial psychologyMedicinePediatrics

Abstract

fetched live from OpenAlex

Background: Nurturing care of young children is aimed at promoting lifelong, intergenerational health and well‐being, as well as social and economic benefits. This study is aimed at qualitatively exploring maternal perceptions related to nurturing care, their access to information and support for caregiving, the home and community environments and practices, and how caregivers promote infants’ health and well‐being in Soweto, South Africa. Methods: The study employed a sequential, two‐stage process. First, three focus group discussions were conducted with a total of 19 mothers of children aged 0–24 months, which then informed 12 in‐depth interviews (four women from each focus group discussion). Focus group discussions and interviews were audio recorded and transcribed verbatim and data were analysed using thematic analysis. Results: The health and well‐being of infants were generally described in relation to their feeding and growth and how physically active they were. The need for pregnancy and caregiving information, accompanied by opportunities to discuss this with a health care worker or other women, was highlighted by participants in this study. Potentially obesogenic and non‐responsive infant and young child feeding practices were commonly reported by mothers. Responsive caregiving was described as taking care of children’s physical needs, providing them with love, and playing with them. Female matriarchs were particularly influential in providing caregiving advice and support for mothers. Naturally occurring interactions, such as talking and singing, were commonly reported practices to promote children’s development in the home. Safety concerns were ubiquitous and limited children’s play and exploration outside the home. Conclusions: This is one of few studies to explore caregivers’ perceptions of nurturing care in the South African context and the first to focus specifically on the first 1000 days. Thus, the study findings can contribute to strengthening initiatives to support caregivers to provide nurturing care for young children in South Africa and other similar contexts. Findings point to the need for better targeted information and support for mothers and other caregivers around nurturing care, especially elements related to infant and young child feeding (including responsive feeding), responsive care, early learning, and how to address safety in the home. There is also a gap in the provision of appropriate information and opportunities to engage with peers and health care workers around issues pertinent to pregnant women within current services. These deficiencies can be addressed through strengthening existing services, leveraging current platforms of care and support for pregnant women and young children, particularly through the health system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.011
GPT teacher head0.261
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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