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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".