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How collaboration in therapy becomes therapeutic: The therapeutic collaboration coding system

2012· article· en· W1744057157 on OpenAlexaff
Eugénia Ribeiro, António P. Ribeiro, Miguel M. Gonçalves, Adam O. Horvath, William B. Stiles

Bibliographic record

VenuePsychology and Psychotherapy Theory Research and Practice · 2012
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsSimon Fraser University
FundersFundação para a Ciência e a Tecnologia
KeywordsCoding (social sciences)MedicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: The quality and strength of the therapeutic collaboration, the core of the alliance, is reliably associated with positive therapy outcomes. The urgent challenge for clinicians and researchers is constructing a conceptual framework to integrate the dialectical work that fosters collaboration, with a model of how clients make progress in therapy. AIM: We propose a conceptual account of how collaboration in therapy becomes therapeutic. In addition, we report on the construction of a coding system - the therapeutic collaboration coding system (TCCS) - designed to analyse and track on a moment-by-moment basis the interaction between therapist and client. Preliminary evidence is presented regarding the coding system's psychometric properties. The TCCS evaluates each speaking turn and assesses whether and how therapists are working within the client's therapeutic zone of proximal development, defined as the space between the client's actual therapeutic developmental level and their potential developmental level that can be reached in collaboration with the therapist. METHOD: We applied the TCCS to five cases: a good and a poor outcome case of narrative therapy, a good and a poor outcome case of cognitive-behavioural therapy, and a dropout case of narrative therapy. CONCLUSION: The TCCS offers markers that may help researchers better understand the therapeutic collaboration on a moment-to-moment basis and may help therapists better regulate the relationship.

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)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.143
GPT teacher head0.503
Teacher spread0.359 · 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.

Study designTheoretical or conceptual
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

Citations123
Published2012
Admission routes1
Has abstractyes

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