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Record W2883630254 · doi:10.31265/jcsw.v12i1.145

Constructing Family from a Social Work Perspective in Child Welfare

2017· article· en· W2883630254 on OpenAlexaffabout
Randy Johner, Douglas Durst

Bibliographic record

VenueJournal of Comparative Social Work · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsSocial workStatuteChild protectionAgency (philosophy)Public relationsFocus groupFamily lifeSociologyWelfareHarmTransformative learningSocial policyQualitative researchPolitical scienceLawGender studiesSocial sciencePedagogy

Abstract

fetched live from OpenAlex

The transformative reality of diverse Canadian families is outpacing national and provincial statutes and policies. Social workers in child welfare agencies are faced with the complex task of making decisions about families while working within the confines of national/provincial statutes and social policies, as well as within agency structures. They attempt to balance the rights of diverse Canadian families and still protect children at risk of harm with the principle of the ‘best interest of the child’. The purpose of this qualitative case study was to explore the construction of ‘family’ and decisions about family life in protection services from the perspective of professional social workers in the prairie region of Canada. Social workers from several urban communities were invited to participate in focus groups. During the focus group discussions, themes of social worker’s nuanced and somewhat fluid understandings of family did not always converge with current legal and professional notions of families. Study findings suggest that social workers’ construction of family and the decisions they make about family life involve three primary themes: ‘acceptance of diverse understandings of family’; ‘safety and the best interest of the child’, and ‘professional discretionary decisions’

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0140.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.424
Teacher spread0.337 · 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.

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

Citations7
Published2017
Admission routes2
Has abstractyes

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