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Record W2110930321 · doi:10.1177/1473325006067365

The Needs of Foster Parents

2006· article· en· W2110930321 on OpenAlexaffabout
Tracy E. MacGregor, Susan Rodger, Anne L. Cummings, Alan W. Leschied

Bibliographic record

VenueQualitative Social Work · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsWestern University
Fundersnot available
KeywordsFoster careFoster parentsAgency (philosophy)PsychologyWelfareFocus groupQualitative researchDevelopmental psychologySocial psychologyNursingMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

This qualitative study examined motivation, support, and retention of foster parents in a child welfare agency in nine Canadian counties. Data from nine foster parent focus groups (54 participants) were categorized into themes for four structured questions. Results showed that the most frequent motivations for being foster parents were intrinsic, altruistic motivators of wanting to make a difference in children's lives and a desire to have children in the home. The most important supports and deficits in support from their agencies were emotional support, trust and good communication with workers, respect for foster parents' abilities and opinions, and being considered part of the child-care team. Strategies to increase retention of foster parents included improving supports for fostering, providing accurate information about the foster child, and introducing foster parents to the role gradually.

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 score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.384
Teacher spread0.339 · 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

Citations204
Published2006
Admission routes2
Has abstractyes

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