Assessing client self-narrative change in emotion-focused therapy of depression: An intensive single case analysis.
Bibliographic record
Abstract
Personality researchers use the term self-narrative to refer to the development of an overall life story that places life events in a temporal sequence and organizes them in accordance to overarching themes. In turn, it is often the case that clients seek out psychotherapy when they can no longer make sense of their life experiences, as a coherent story. Angus and Greenberg (L. Angus and L. Greenberg, 2011, Working with narrative in emotion-focused therapy: Changing stories, healing lives. Washington, DC: American Psychological Association Press) view the articulation and consolidation of an emotionally integrated self-narrative account as an important part of the therapeutic change process that is essential for sustained change in emotion-focused therapy of depression. The purpose of the present study was to investigate client experiences of change, and self-narrative reconstruction, in the context of one good outcome emotion-focused therapy dyad drawn from the York II Depression Study. Using the Narrative Assessment Interview (NAI) method, client view of self and experiences of change were assessed at three points in time--after session one, at therapy termination, and at 6 months follow-up. Findings emerging from an intensive narrative theme analyses of the NAI transcripts--and 1 key therapy session identified by the client--are reported and evidence for the contributions of narrative and emotion processes to self-narrative change in emotion-focused therapy of depression are discussed. Finally, the implications of assessing clients' experiences of self-narrative change for psychotherapy research and practice are addressed.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".