The relation between outcome expectation, therapeutic alliance, and outcome among depressed patients in group cognitive-behavioral therapy
Bibliographic record
Abstract
OBJECTIVE: Although patients' expectation for improvement correlates with their treatment outcome, there remains limited information regarding the mechanisms through which outcome expectation influences outcome. Although several studies have revealed alliance as a mediator of the expectancy-outcome relation, most have focused on individual psychotherapy only. More research is needed examining mediators, including alliance quality, of the outcome expectation-outcome relation in group therapy. METHOD: This study focused on such associative chains among 91 depressed outpatients who completed 10 weeks of group cognitive-behavioral therapy. We conducted simple and multiple mediation analyses, accounting for the nested data structure. RESULTS: As predicted, we found: (i) The relations between baseline outcome expectation and both posttreatment anxiety and depression were mediated by alliance quality; (ii) the early therapy outcome expectation-posttreatment anxiety relation was mediated by mid-treatment alliance; (iii) the relation between early alliance and posttreatment interpersonal problems was mediated by during-therapy outcome expectation; and (iv) the relation between baseline outcome expectation and posttreatment interpersonal problems was mediated by two variables acting in turn, early alliance and during-therapy outcome expectation. All other tested models were not significant. CONCLUSIONS: Results suggest that bidirectional relations between outcome expectation and alliance, with both directions influencing outcome. Clinical and empirical implications 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".