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Record W2999331432 · doi:10.1080/17518423.2020.1711542

Exploring Relational Dialogue in Solution-Focused Coaching Sessions: An Analysis of Co-Construction and Reflection

2020· article· en· W2999331432 on OpenAlexaff
Gillian King, Patricia Baldwin, Michelle Servais, Sheila Moodie, Janet Kim

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2020
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsThames Valley Children's CentreWestern UniversityHolland Bloorview Kids Rehabilitation HospitalUniversity of Toronto
Fundersnot available
KeywordsCoachingActive listeningSession (web analytics)PsychologyReflection (computer programming)Action (physics)Meaning (existential)Applied psychologyPsychotherapistComputer science

Abstract

fetched live from OpenAlex

Purpose: To examine solution-focused coaching (SFC) as a means to enhance clinicians’ professional development.Methods: Six pediatric rehabilitation clinicians (three physical, two occupational, and one behavior therapist) each received two SFC sessions targeting clinical listening goals. Conversational intervals were noted in session transcriptions. Frequencies of relational strategies and conversational intervals were calculated. The meaning of intervals > 10 s was examined.Results: The most frequent relational strategies indicated that SFC facilitates reflection and critical thinking, and encourages action. An appreciable number of long intervals (>10 s) occurred, indicating substantial reflection by participants. These were embedded in relational dialogue sequences involving coach questions and formulations, and participant pauses.Conclusions: The findings support the use of SFC as a professional development tool and substantiate the view that SFC ‘works’ through the coach’s use of relational strategies designed to facilitate collaborative conversations that build solutions through an emphasis on reflection and action.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.112
GPT teacher head0.326
Teacher spread0.213 · 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.

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

Citations7
Published2020
Admission routes1
Has abstractyes

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