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Record W2902274517 · doi:10.1080/02650533.2018.1553870

Enhancing supervision in children’s mental health through Bowen’s Family of Origin supervisory training

2018· article· en· W2902274517 on OpenAlexaffabout
Beth Archer‐Kuhn, Patricia Samson

Bibliographic record

VenueJournal of Social Work Practice · 2018
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMental healthThematic analysisPsychologyVulnerability (computing)AllianceTraining (meteorology)Applied psychologyDevelopmental psychologyMedical educationQualitative researchPsychotherapistMedicineSociology

Abstract

fetched live from OpenAlex

Bowen’s Family of Origin training is extended to supervisory training in a Western Canada children’s mental health setting with new team leaders. The training focused on worker life experiences and how they bring those experiences to the supervisory relationship to influence work with children, youth and families. Participant vulnerability, inherent in the training, provides significant insight for them in their leadership role to reveal awareness of self and other; how awareness of family of origin patterns can reduce stress and increase relationships across the organisation supporting therapeutic alliance.One-on-one interviews were audio recorded and transcribed. Inductive thematic analysis identifies three emerging themes that speak to the awareness of self and other, shifting understandings of the supervisory role, and a parallel process of peer support. Findings are presented using participant quotes, followed by a discussion of implications for supervision within children’s mental health.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0000.001
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 head0.063
GPT teacher head0.385
Teacher spread0.322 · 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 designObservational
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

Citations1
Published2018
Admission routes2
Has abstractyes

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