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Record W2000608481 · doi:10.1521/jsyt.2014.33.4.62

Opportunities: Organizing the Solution-Focused Interview

2014· article· en· W2000608481 on OpenAlex
Lance Taylor, Joel Simon

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of Systemic Therapies · 2014
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsMicromolding Solutions (Canada)Cochrane
Fundersnot available
KeywordsInterviewSolution focused brief therapySelection (genetic algorithm)Action (physics)Articulation (sociology)PsychologyEngineering ethicsKnowledge managementApplied psychologyPsychotherapistComputer scienceSociologyArtificial intelligenceEngineeringPolitical science

Abstract

fetched live from OpenAlex

In solution-focused brief therapy, the client is considered the expert in identifying his or her hopes and goals. The interviewer's role is to facilitate the articulation of hopes and the building of these hopes into change. This article shows how each client action presents multiple opportunities for solution-focused therapists to perform their role. Microanalysis of actual therapeutic dialogue, by two collaborating practitioners, reveals how opportunities are identified, how options for solution-focused responding are generated, how the preferred opportunities and responses are selected, and how rationales for the particular selection may be usefully shared and compared. The most practical application for this study of opportunities lies in the continual refinement of solution-focused interviewing skills in the contexts of training, supervision, and perpetual learning.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.064
GPT teacher head0.286
Teacher spread0.222 · 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