Training community-based psychotherapists to maintain a therapeutic alliance: A psychotherapy practice research network study.
Bibliographic record
Abstract
The goal of this study was to test the efficacy of training community-based psychotherapists who were part of a practice research network to be more attuned to their patients' experiences of the therapeutic relationship. We were particularly interested in the effect of therapist training on the congruence of alliance ratings with their patients. Forty psychotherapists who treated 117 patients were randomly assigned to receive either no training or training, whose learning objectives were to help therapists to develop and maintain a therapeutic alliance. The training included workshops and ongoing consultations to help the clinician to strengthen the therapeutic relationship with the use of mentalizing, attachment theory, countertransference management, and metacommunication. Therapeutic alliance and well-being outcomes were measured at each of six consecutive early psychotherapy sessions. We used the truth and bias model and response surface analysis within a multilevel modeling context to test hypotheses. There was a significantly faster rate of alliance growth in the training versus the no training condition when the alliance was rated by therapists, but not when rated by patients. Trained therapists experienced greater temporal congruence in alliance ratings with their patients compared to untrained therapists. Patient well-being outcomes improved in a session when trained therapists and their patients agreed in their positive alliance ratings in a previous session. This association not significant among untrained therapists. Training therapists in key interpersonally focused skills may lead them to be better attuned to their patients' experiences of the therapeutic relationship. (PsycInfo Database Record (c) 2023 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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".