Emotional Processing, Interaction Process, and Outcome in Clarification-Oriented Psychotherapy for Personality Disorders: A Process-Outcome Analysis
Bibliographic record
Abstract
It is important to understand the change processes involved in psychotherapies for patients with personality disorders (PDs). One patient process that promises to be useful in relation to the outcome of psychotherapy is emotional processing. In the present process-outcome analysis, we examine this question by using a sequential model of emotional processing and by additionally taking into account a therapist's appropriate responsiveness to a patient's presentation in clarification-oriented psychotherapy (COP), a humanistic-experiential form of therapy. The present study involved 39 patients with a range of PDs undergoing COP. Session 25 was assessed as part of the working phase of each therapy by external raters in terms of emotional processing using the Classification of Affective-Meaning States (CAMS) and in terms of the overall quality of therapist-patient interaction using the Process-Content-Relationship Scale (BIBS). Treatment outcome was assessed pre- and post-therapy using the Global Severity Index (GSI) of the SCL-90-R and the BDI. Results indicate that the good outcome cases showed more self-compassion, more rejecting anger, and a higher quality of therapist-patient interaction compared to poorer outcome cases. For good outcome cases, emotional processing predicted 18% of symptom change at the end of treatment, which was not found for poor outcome cases. These results are discussed within the framework of an integrative understanding of emotional processing as an underlying mechanism of change in COP, and perhaps in other effective therapy approaches for PDs.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".