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Good helping relationships in child welfare: learning from stories of success

2006· article· en· W1982582369 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChild & Family Social Work · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsDyadWelfarePsychologySample (material)Qualitative researchSocial psychologyPower (physics)Social workDevelopmental psychologyApplied psychologyMedical educationSociologyMedicineSocial science

Abstract

fetched live from OpenAlex

ABSTRACT This study involved in‐depth exploration of good helping relationships in child welfare. A select sample of six child welfare worker–client dyads was interviewed to determine worker attributes and actions that were key to the development of good working relationships. Innovative features of the research design, such as a multiple interview format with two individual and one joint interview for each worker and client (five interviews per dyad) and opportunities for the worker and client in each dyad to reflect on and respond to the other’s interview transcripts, produced rich data and revealed high levels of congruency among workers, clients and researchers about worker relationship competencies. Two categories of themes that emerged from the qualitative analysis are discussed: (1) soft, mindful and judicious use of power; and (2) humanistic attitude and style that stretches traditional professional ways‐of‐being. Implications for the hiring, education and training, and supervision of child welfare workers are presented.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.303
Teacher spread0.269 · 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