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Record W4413582313 · doi:10.1080/01488376.2025.2551236

Money in Foster Care: An Exploration of How Foster Care Grants Are Used By Foster Parents in South Africa

2025· article· en· W4413582313 on OpenAlexaff
Olebogeng Tladi, Sipho Sibanda

Bibliographic record

VenueJournal of Social Service Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsFoster careFoster parentsBusinessSocial workPsychologyEconomic growthNursingMedicineEconomics

Abstract

fetched live from OpenAlex

Foster care is a form of alternative care arrangement for children in need of care and protection. Upon the placement of a child in foster care, a grant is given to foster parents to provide for the needs of that child. A qualitative approach was adopted to explore how the foster parents in South Africa utilize the foster child grant. Purposive sampling was used to select twenty participants for the study. Data was collected from 10 foster children and 10 foster parents through semi-structured interviews and focus group discussions, and analyzed using thematic analysis. The findings revealed that both foster parents and foster children make decisions about what to use the foster care grant on. However, some foster parents do not spend the grant as expected. The foster care grant is meant to assist in the upbringing of foster children and not as an extra income to the parents. The study concludes that the grant should be increased as the current amount does not meet all the children’s needs and that social workers should intensify monitoring and supervision of foster care placements. It is recommended that future studies should develop a foster care grant supervision and monitoring framework.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.067
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.112
GPT teacher head0.395
Teacher spread0.283 · 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 teacher head, 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

Citations4
Published2025
Admission routes1
Has abstractyes

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