Testing a model of change in the experiential treatment of depression.
Bibliographic record
Abstract
In this study, we measured emotional processing and the alliance across 3 phases of therapy (beginning, working, and termination) for 74 clients who each received brief experiential psychotherapy for depression. Using path analysis, we proposed and tested a model of relationships between these 2 processes across phases of therapy and how these processes relate to predict improvement in the domains of depressive and general symptoms, self-esteem, and interpersonal problems after experiential treatment. Both therapy processes significantly increased across phases of therapy. Controlling for both client processes at the beginning of therapy, working phase emotional processing was found to directly and best predict reductions in depressive and general symptoms, and it could directly predict gains in self-esteem. Within working and termination phases of therapy, the alliance significantly contributed to emotional processing and indirectly contributed to outcome. Surprisingly, beginning therapy alliance (measured after Session 1) also directly predicted all outcomes. Furthermore, only clients' beginning therapy process predicted reductions in interpersonal problems. Therefore, although the proposed theory of change was supported, clients' beginning therapy processes may constrain clients' success in experiential treatment and in particular their outcomes in some problem domains related to depression.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".