Contrasting Two Clients in Emotion-Focused Therapy for Depression 1: The Case of "Tom," "Trapped in the Tunnel"
Bibliographic record
Abstract
The objective in this paper is to present a case drawn from a series of randomized clinical trials (RCT) comparing brief (16-20 sessions), emotion-focused therapy (EFT) the process experiential approach with client-centered therapy and cognitive-behavioral therapy in the treatment of depression. A case comparison method that triangulates data from clients’ histories, in-session process, post-session questionnaires, and post-therapy outcome measures was used to increase understanding of those factors that contribute to successful and unsuccessful outcomes. The case comparison method examines the role of the working alliance, clients’ emotional processing, clients’ and therapists’ interpersonal processes, and clients’ cognitive processing, as well as the specific changes that clients report immediately following their sessions that have been found to be related to therapeutic outcomes. The method is illustrated with two examples one a poor outcome case and the other a good outcome case The poor outcome case of "Tom" is presented in this article, and the good outcome case of "Eloise" is presented in the next article by Goldman, Watson, and Greenberg (2011). These two cases extend and build on the cases presented by the authors in their book Case Studies in Emotion-Focused Treatment of Depression: A Comparison of Good and Poor Outcome (Watson, Goldman, & Greenberg, 2007), in which six clients, three good outcome and three poor outcome, were 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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| 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".