Self-narrative reconstruction in emotion-focused therapy: A preliminary task analysis
Bibliographic record
Abstract
OBJECTIVE: This research explored the consolidation phase of emotion-focused therapy (EFT) for depression and studies-through a task-analysis method-how client-therapist dyads evolved from the exploration of the problem to self-narrative reconstruction. METHOD: Innovative moments (IMs) were used to situate the process of self-narrative reconstruction within sessions, particularly through reconceptualization and performing change IMs. We contrasted the observation of these occurrences with a rational model of self-narrative reconstruction, previously built. RESULTS: This study presents the rational model and the revised rational-empirical model of the self-narrative reconstruction task in three EFT dyads, suggesting nine steps necessary for task resolution: (1) Explicit recognition of differences in the present and steps in the path of change; (2) Development of a meta-perspective contrast between present self and past self; (3) Amplification of contrast in the self; (4) A positive appreciation of changes is conveyed; (5) Occurrence of feelings of empowerment, competence, and mastery; (6) Reference to difficulties still present; (7) Emphasis on the loss of centrality of the problem; (8) Perception of change as a gradual, developing process; and (9) Reference to projects, experiences of change, or elaboration of new plans. CONCLUSIONS: Central aspects of therapist activity in facilitating the client's progression along these nine steps are also elaborated.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".