Integrating Theory and Practice: Using Microanalysis in Psychotherapy Training to Transform Abstract Concepts Into Specific Behaviors
Bibliographic record
Abstract
Integrating theory and psychotherapy practice in academic training is a challenge. Theoretical concepts and learning objectives are often vague, and how they come into practice can sometimes be difficult to grasp. This article describes how microanalysis was used with students to explore how theoretically abstract concepts, in this case “empathy,” manifest in their clinical practice. We watched two types of videorecorded psychotherapy sessions, the students analyzed their own work and shared collected examples of “empathic” therapist behaviors, and, finally, we listed what we found and discussed the results. Together, we aimed to create clear and specific behavioral descriptions from a vague concept and to use those descriptions as a basis for reflective learning. When skills are described in overt behavioral terms, they can be taught and evaluated. Using microanalysis in teaching can facilitate understanding abstract concepts, support the transition from theoretical understanding to implementation in clinical practice, and offer a tool for the clinical practitioner to continually develop their own work individually or in groups.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| 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".