Using Microanalysis in Solution-Focused Psychotherapy Training: A Description of Two Thesis Modules
Bibliographic record
Abstract
This article describes the implementation of microanalysis in two Finnish psychotherapy training programs. Thirty-seven psychotherapy trainees were guided through the process of writing a thesis that applied microanalysis to video recordings of their clinical practice, Trainees reported significant professional growth and improved therapeutic skills from the process, despite language, technical, and knowledge challenges. Using microanalysis, the utterance-by-utterance examination of face-to-face dialogue enhanced the focus on observable behavior, consistent with solution-focused principles. The method also helped trainers assess the trainees' skills and encouraged trainees to pay closer attention to interaction in their practice. The purpose here is to present the rationale, pedagogical approach, structure, and content of the thesis module, ending with a brief description of some trainees' theses. The positive outcomes suggest that microanalysis can be applied in various relational training contexts. The microanalytic principles might also be useful for clinical supervisors and practitioners to develop their practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".