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Record W4408210488 · doi:10.1080/0312407x.2025.2462304

Supervision on Country: Enhancing Culturally Safe Social Work Supervision Through First Nations Knowledges

2025· article· en· W4408210488 on OpenAlexaboutno aff
Jamie Sorby, Rebecca Regan‐Coe, Carole Zufferey, Nicole Moulding

Bibliographic record

VenueAustralian Social Work · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
FundersAustralian Association of Social Workers
KeywordsSocial workWork (physics)Culturally sensitiveSociologyPublic relationsPsychologyPolitical scienceSocial psychologyLawEngineering

Abstract

fetched live from OpenAlex

There has been limited scholarship on culturally responsive supervision in social work. This article provides a comprehensive review of literature and builds artefacts and conceptual maps encompassing key cultural elements in cultural-clinical supervision to inform social work practice. The visual artefacts present five key areas of Aboriginal ways of knowing, being, and doing, including identity, community, relationality, deep listening, and yarning. This project was Aboriginal-led and developed collaboratively between Aboriginal and non-Aboriginal social workers and researchers. This article can assist both First Nations and non-First Nations supervisors to better understand the importance of culturally responsive supervision in all social work settings.IMPLICATIONSIt is important for social workers to incorporate Aboriginal ways of knowing, being, and doing in their supervision practices, to create culturally safe supervision.Bringing the cultural-clinical interface together can enhance cultural supervision in all services.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.784
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.009
Science and technology studies0.0200.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.045
GPT teacher head0.377
Teacher spread0.332 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations2
Published2025
Admission routes1
Has abstractyes

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