How central is the alliance in psychotherapy? A multilevel longitudinal meta-analysis.
Bibliographic record
Abstract
Prior meta-analyses have found a moderate but robust relationship between alliance and outcome across a broad spectrum of treatments, presenting concerns, contexts, and measurements. However, there continues to be a lively debate about the therapeutic role of the alliance, particularly in treatments that are tested using randomized clinical trial (RCT) designs. The purpose of this present study was to examine whether research design, type of treatment, or author's allegiance variables, alone or in combination, moderate the relationship between alliance and outcome. Multilevel longitudinal analysis was used to investigate the following moderators of the alliance-outcome correlation: (a) research design (RCT or other), (b) use of disorder-specific manuals, (c) specificity of outcomes, (d) cognitive and/or behavioral therapy (CBT) or other types of treatments, (e) researcher allegiance, and (f) time of alliance assessment. RCT, disorder-specific manual use, specificity of primary and secondary outcomes, and CBT did not moderate the alliance-outcome correlation. Early alliance-outcome correlations were slightly higher in studies conducted by investigators with specific interest in alliance than were those in studies conducted by researchers without such an allegiance. Over the course of therapy, these initial differences disappeared. Apart from this trend, none of the variables previously proposed as potential moderators or mediators of the alliance-outcome relation, alone or in combination, were found to have a mediating impact.
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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 | 0.054 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.038 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".