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Record W2165555936 · doi:10.1521/jsyt.2014.33.1.48

Solution-Focused Responses to “No Improvement”: A Qualitative Analysis of the Deconstruction Process

2014· article· en· W2165555936 on OpenAlexvenueno aff
Andrés Sánchez-Prada, Mark Beyebach

Bibliographic record

VenueJournal of Systemic Therapies · 2014
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsDeconstruction (building)GeneralitySolution focused brief therapyProcess (computing)PsychologySession (web analytics)Qualitative researchConsolidation (business)PsychotherapistComputer scienceSociologyEngineeringSocial scienceBusiness

Abstract

fetched live from OpenAlex

When a client reports no improvement since the previous session, one response for the therapist can be to deconstruct this description and seek improvements, however small. A qualitative, discovery-oriented study examined the process of deconstruction in eight solution-focused brief therapy sessions where clients had initially reported no improvement. The findings suggested that the deconstruction of initial reports of no improvement is a complex process in which therapists do not follow a single path but respond in a flexible way to their clients' discourse: They may move directly into deconstruction, elaboration, and consolidation or may begin indirectly by first connecting with the negative report and preparing for deconstruction. Overall, maintaining positive (versus negative) topics in the conversations is important, but other therapeutic topics can be helpful at some points. It may also be useful to move systematically along a specificity-generality continuum, whether from specific episodes to general evaluations or the reverse.

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)0.042
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.042
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.069
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0070.018
Scholarly communication0.0050.006
Open science0.0030.008
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.348
Teacher spread0.328 · 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 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

Citations6
Published2014
Admission routes1
Has abstractyes

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