Does emotional processing predict 18-month post-therapy outcomes in the experiential treatment of major depression?
Bibliographic record
Abstract
Objective: This study tested whether emotion-focused therapy’s (EFT) emotion processing theory serves as a predictor of 18-month post-therapy outcomes for major depressive disorder (MDD), independent of experiential therapy received. Method: We examined sequences of emotion episodes using the THEME™ sequential analysis of emotional processing in 55 clients who provided 18-month post-therapy Beck Depression Inventory reports after receiving experiential treatment in the York I and II trials, either emotion-focused or client-centered therapy. Archival Classification of Affective Meaning States (CAMS) ratings of emotion episodes of clients’ working-phase sessions were analyzed using THEME™ sequential analyses of emotions coded during emotion episodes. Results: According to THEME™, poor outcome clients (Beck Depression Inventory at 18 months ≥ 10) expressed more emotion episode sequences containing secondary, or self-protective emotions, than good outcome clients. Good outcome clients expressed more emotion sequences with needs, hurt/grief, and assertive anger than poor outcome clients. Conclusions: EFT sequential emotional processing theory appears to offer good basic assumptions for experiential long-term therapy outcomes after receiving therapy for MDD. Generalization of the theory for other treatments is desired.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".