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Record W2088142876 · doi:10.1080/10503307.2010.514960

Narrative change in emotion-focused therapy: How is change constructed through the lens of the innovative moments coding system?

2010· article· en· W2088142876 on OpenAlex

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.

Bibliographic record

VenuePsychotherapy Research · 2010
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsYork University
Fundersnot available
KeywordsNarrativeConversationNarrative therapyCoding (social sciences)PsychologyOutcome (game theory)PsychotherapistAction (physics)SociologyLinguisticsCommunicationSocial science

Abstract

fetched live from OpenAlex

The aim of this study was to advance understanding of how clients construct their own process of change in effective therapy sessions. Toward this end, the authors applied a narrative methodological tool for the study of the change process in emotion-focused therapy (EFT), replicating a previous study done with narrative therapy (NT). The Innovative Moments Coding System (IMCS) was applied to three good-outcome and three poor-outcome cases in EFT for depression to track the innovative moments (IMs), or exceptions to the problematic self-narrative, in the therapeutic conversation. IMCS allows tracking of five types of IMs events: action, reflection, protest, reconceptualization, and performing change. The analysis revealed significant differences between the good-outcome and poor-outcome groups regarding reconceptualization and performing change IMs, replicating the findings from a previous study. Reconceptualization and performing change IMs seem to be vital in the change process.

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)0.002
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 score0.313
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.181
GPT teacher head0.421
Teacher spread0.240 · 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