Predictors of patient cognitive therapy skills and symptom change in two randomized clinical trials: The role of therapist adherence and the therapeutic alliance.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.034 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it