Repurposing process measures to train psychotherapists: Training outcomes using a new approach
Bibliographic record
Abstract
Abstract Aims: First, this paper presents the rationale for a novel approach to training counsellors in which measures for psychotherapy process research are taught to students before moving on to teaching basic empathic reflections and interventions. The rationale for this is that client process measures can be re‐purposed to help orient and sensitise trainees to key in‐session moments. Second, we present a training outcome study that assesses the effectiveness of this approach. Method: Using an experiential‐integrative therapy approach, a 13‐week training program was used to teach psychotherapy skills and process research measures to22 clinical graduate students taken from two cohorts. As part of the course, trainees conducted several single sessions with volunteer clients on four separate occasions. Training outcomes were measured using both trainee and client reports. Results: Compared to baseline, therapists reported significant and steady gains (all p's<.05) in session management, reducing their anxious self‐awareness, and in improved sense of self‐efficacy, with the latter having the largest effect (partial Eta Sq.=.381). Discussion: While the findings provide some support for a new training strategy, a dismantling design is needed next to more closely examine the process‐measure approach to training.
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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 | 0.030 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".