Ruptures and repairs of group therapy alliance. An untold story in psychotherapy research
Bibliographic record
Abstract
Although previous studies investigated the characteristics of therapeutic alliance in group treatments, there is still a dearth of research on group alliance ruptures and repairs. The model by Safran and Muran was originally developed to address therapeutic alliance in individual therapies, and the usefulness of this approach to group intervention needs to be demonstrated. Alliance ruptures are possible at member to therapist, member to member, member to group levels. Moreover, repairs of ruptures in group are quite complex, i.e., because other group members have to process the rupture even if not directly involved. The aim of the current study is to review the empirical research on group alliance, and to examine whether the rupture repair model can be a suitable framework for clinical understanding and research of the complexity of therapeutic alliance in group treatments. We provide clinical vignettes and commentary to illustrate theoretical and research aspects of therapeutic alliance rupture and repair in groups. Our colleague Jeremy Safran made a substantial contribution to research on therapeutic alliance, and the current paper illustrates the enduring legacy of this work and its potential application to the group therapy context.
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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 | 0.052 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.097 |
| Scholarly communication | 0.011 | 0.070 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.010 | 0.019 |
| 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".