Can alliance-focused supervision help improve emotional involvement and collaboration between client and therapist?
Bibliographic record
Abstract
As research has shown a consistent link between the alliance and the therapeutic success, an alliance-focused supervision was designed to help novice therapists improve their relational and collaborative abilities. Fifteen young therapists participated in the alliance-focused supervision and 15 did not. Therapists’ and clients’ results at the Working Alliance Inventory showed that the supervision model improved their perceptions concerning the therapist’s ability to foster mutual emotional involvement, as well as collaboration. All therapists demonstrated an ability to negotiate emotional alliance with the client. However, when they participated in the alliance-focused supervision, they appeared to improve their capacity to finding an agreement with the client around therapeutic goals, which was not the case when they did not participate. In addition, more independent views on the alliance appeared in clients and therapists when the professional took part in the alliance-focused supervision than when he/she did not. Results on the Difficulties in Emotion Regulation Scale also showed that participating in the alliance-focused supervision had a protective influence on therapists’ impulsiveness. Overall, alliance-focused supervision appeared useful in helping therapists improve emotional involvement and collaboration in the alliance. It also seemed to protect them from being impulsive.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".