Toward an integrative understanding of narrative and emotion processes in Emotion-focused therapy of depression: Implications for theory, research and practice
Bibliographic record
Abstract
This paper addresses the fundamental contributions of client narrative disclosure in psychotherapy and its importance for the elaboration of new emotional meanings and self understanding in the context of Emotion-focused therapy (EFT) of depression. An overview of the multi-methodological steps undertaken to empirically investigate the contributions of client story telling, emotional differentiation and meaning-making processes (Narrative Processes Coding System; Angus et al., 1999) in EFT treatments of depression is provided, followed by a summary of key research findings that informed the development of a narrative-informed approach to Emotion-focused therapy of depression (Angus & Greenberg, 2011). Finally, the clinical practice and training implications of adopting a research-informed approach to working with narrative and emotion processes in EFT are described, and future research directions discussed.
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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 | 0.026 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".