Working with narrative in emotion-focused therapy: Changing stories, healing lives.
Bibliographic record
Abstract
"In psychotherapy, as in life, all significant emotions are embedded in important stories, and all significant stories revolve around important emotional themes. Yet, despite the interaction between emotion and narrative processes, emotion-focused therapy (EFT) and narrative-informed therapies have evolved as separate clinical approaches. In this book, Lynne Angus and Leslie Greenberg address this gap and present a groundbreaking, empirically based model that integrates working with narrative and emotion processes in EFT. According to Angus and Greenberg's narrative-informed approach to EFT, all successful psychotherapy entails the articulation, revision, and deconstruction of clients' maladaptive life stories in favor of more life-enhancing alternatives. Because emotions and narratives interact to form meaning and sense of self, the evocation and articulation of emotions is critical to changing life narratives. Individual chapters describe how the interaction between emotion and narrative creates a constantly evolving sense of self; how clinicians can address both narrative and emotion processes to help clients create more adaptive, empowering meanings and sense of self; and the importance of a strong therapeutic alliance. Engaging, in-depth case studies at the end of the book illustrate how the model can be applied to treatment of depression and emotional trauma"--Publicity materials. (PsycINFO Database Record (c) 2011 APA, all rights reserved)
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".