Using Microanalysis of Communication to Compare Solution-Focused and Client-Centered Therapies
Bibliographic record
Abstract
Microanalysis in psychotherapy is the close examination of the moment-by-moment communicative actions of the therapist. This study microanalyzed demonstration sessions by experts on solution-focused and client-centered therapies, specifically, the first 50 therapist utterances of sessions by Steve de Shazer, Insoo Kim Berg, Carl Rogers, and Nathaniel Raskin. The first analysis examined how the therapist communicated, namely, whether the therapist's contribution took the form of questions or of formulations (e.g., paraphrasing). The second analysis rated whether each question or formulation was positive, neutral, or negative. Two analysts demonstrated high-independent-agreement for both methods. Results showed that the solution-focused and client-centered experts differed in how they structured the sessions: The client-centered therapists used formulations almost exclusively, that is, they responded to client's contributions. Solution-focused experts used both formulations and questions, that is, they both initiated and responded to client contributions. They also differed in the tenor of their contributions: The solution-focused therapists' questions and formulations were primarily positive, whereas those of the client-centered therapists were primarily negative and rarely neutral or positive. Microanalysis can complement outcome research by providing evidence about what therapists do in their sessions.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".