Predictors of patient cognitive therapy skills and symptom change in two randomized clinical trials: The role of therapist adherence and the therapeutic alliance.
Bibliographic record
Abstract
OBJECTIVE: Previous research has found that therapist adherence to concrete, problem-focused cognitive therapy (CT) techniques predicts depressive symptom change (e.g., Feeley, DeRubeis, & Gelfand, 1999). More recently, Strunk, DeRubeis, Chui, and Alvarez (2007) demonstrated that in-session evidence of patients' use of CT skills was related to a lower rate of relapse in the year following CT for depression. The current investigation attempts to integrate and extend these findings within 2 separate samples of patients and therapists. METHOD: Drawing from the CT samples (N = 105, mean age = 40 years, female = 62%, White = 82%) of 2 published randomized clinical trials of depression treatment, we conducted analyses to examine whether therapist adherence to concrete CT techniques (Collaborative Study Psychotherapy Rating Scale) and the quality of the therapeutic alliance (Working Alliance Inventory) predict patients' use of CT skills (Performance of Cognitive Therapy Strategies) and subsequent Beck Depression Inventory symptom change. RESULTS: Results indicated a differential pattern of prediction in the 2 samples. In one, CT techniques exhibited a stronger association with patient CT skills and symptom change than did the alliance, whereas the reverse pattern emerged in the second sample. A baseline symptom severity × CT techniques interaction indicated that between-study differences in intake depression severity might in part explain the process-outcome differences. CONCLUSIONS: The present findings suggest that the nature of the therapy sample examined may moderate process-outcome findings in psychotherapy research. The implications of these results and directions for future research are discussed.
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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 | 0.086 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".