Contrasting Two Clients in Emotion-Focused Therapy for Depression 2: The Case of "Eloise," "It's Like Opening the Windows and Letting the Fresh Air Come In"
Bibliographic record
Abstract
This paper presents a good-outcome case of "Eloise," an individual drawn from the York II Depression study and treated with emotion-focused therapy (EFT) (Goldman, Greenberg, & Angus, 2006). Using the case comparison method, this study considers data from an observer-rated measure of emotional processing during therapy, the client's perceptions of change as measured by post-session and post-therapy questionnaires, the therapist's perceptions of change as measured by post-session reports, and post-therapy interview data, to form an understanding of factors that contributed to change. Eloise's case study is designed to compare and contrast with Watson, Goldman, and Greenberg's (2011) case study of Tom, a poor-outcome case drawn from a similar RCT. The Eloise and Tom case studies extend and build upon the cases presented by the authors of Case Studies in Emotion-Focused Treatment of Depression: A Comparison of Good and Poor Outcome (Watson, Goldman, & Greenberg, 2007), which consist of three good outcome and three poor outcome clients compared and contrasted using the case-comparison method.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| 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".