The alliance in adult psychotherapy: A meta-analytic synthesis.
Bibliographic record
Abstract
The alliance continues to be one of the most investigated variables related to success in psychotherapy irrespective of theoretical orientation. We define and illustrate the alliance (also conceptualized as therapeutic alliance, helping alliance, or working alliance) and then present a meta-analysis of 295 independent studies that covered more than 30,000 patients (published between 1978 and 2017) for face-to-face and Internet-based psychotherapy. The relation of the alliance and treatment outcome was investigated using a three-level meta-analysis with random-effects restricted maximum-likelihood estimators. The overall alliance-outcome association for face-to-face psychotherapy was r = .278 (95% confidence intervals [.256, .299], p < .0001; equivalent of d = .579). There was heterogeneity among the effect sizes, and 2% of the 295 effect sizes indicated negative correlations. The correlation for Internet-based psychotherapy was approximately the same (viz., r = .275, k = 23). These results confirm the robustness of the positive relation between the alliance and outcome. This relation remains consistent across assessor perspectives, alliance and outcome measures, treatment approaches, patient characteristics, and countries. The article concludes with causality considerations, research limitations, diversity considerations, and therapeutic practices. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".