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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".