Narrative change in emotion-focused therapy: How is change constructed through the lens of the innovative moments coding system?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".