Bibliographic record
Abstract
In Emotion-Focused Therapy for Depression, Leslie S. Greenberg and Jeanne C. Watson, well-regarded scholars and leading figures in the field, provide a manual for the emotion-focused treatment (EFT) of depression. Their approach is supported by studies in which EFT for depression was compared with Cognitive-Behavioral Therapy, Client-Centered Therapy, and then both. The approach has been refined to apply specifically to the treatment of this pervasive and often intractable disorder. The authors discuss the nature of depression and its treatment, examine the role of emotion, present a schematic model of depression and an overview of the course of treatment, and suggest who might benefit. Written with a practical focus rather than the more academic theoretical style of previous books that established the theoretical grounds and scientific viability of working with emotion in psychotherapy, this book aims to introduce practitioners to the idea of using this approach to work with a depressed population. The book covers theory, case formulation, treatment, and research in a way that makes this complex form of therapy accessible to all readers. Particularly valuable are the case examples, which demonstrate the deliberate and skillful use of techniques to leverage emotional awareness and thus bring about change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".