Detecting alliance ruptures: the effects of the therapist’s experience, attachment, empathy and countertransference management skills
Bibliographic record
Abstract
Accurate alliance rupture detection is a prerequisite to any successful repair process. Despite its importance, however, rupture detection remains a struggle for most therapists. Supporting the existence of a therapist effect on therapy outcomes, rupture detection skills may rely on certain therapists' personal characteristics. The aim of this study was to verify whether alliance rupture detection performance is related to therapists' personal characteristics. One hundred and eight undergraduates, trainees and mental health professionals participated in an experimental task assessing their alliance rupture detection ability. Participants also completed attachment, empathy and countertransference management self-reported measures. Participants with clinical experience (trainees and professionals) reported more alliance ruptures, accurate or not, than those with no clinical experience (undergraduates). Trainees reported more accurate ruptures and less inaccurate ones than the two other groups. Attachment anxiety was positively associated with accurate ruptures detection for undergraduates, while this association proved negative for trainees and therapists. Perspective-taking, a cognitive dimension of empathy, was negatively associated with accurate rupture detection, whereas personal distress, an affective dimension of empathy, was negatively associated with accurate ruptures detection for trainees, and positively associated for undergraduates. Self-insight, a component of countertransference management, revealed a negative association with accurate rupture detection for trainees. These findings suggest that therapists vary as to their rupture detection ability and that this ability is related to certain personal characteristics. They also highlight the importance of specific training and clinical supervision for both trainees and experienced therapists in order to improve their detection ability.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".