Using Micro-analyzing Tools to Investigate Therapist Skills in Emotionally Focused Couples Therapy With Couples in a High-Conflict Relationship
Bibliographic record
Abstract
The study focuses on the initial phase of emotionally focused therapy (EFT) and explores techniques and skills therapists employ to break and de-escalate conflictual cycles in relationships. Using micro-analysis, the researchers examined a 50-minute therapy session with a couple in a high-conflict relationship that was conducted by Dr. Susan Johnson. The research team identified and classified the therapist's skills with moment-by-moment interactional processes. A tiering system was developed to examine skills. A total of 404 therapist skills were analyzed. We observed reflecting 90 times, reframing 84 times, cycle work 56 times, validating 50 times, asking evocative questions 48 times, accessing underlying emotions 32 times, heightening emotions 28 times, and enactmentlike skills 16 times. Results showed that the therapist combined active listening methods with EFT-specific strategies such as accessing underlying emotions, highlighting emotions, tracking interactional cycles, and facilitating communication via enactments. Findings are discussed along with implications for clinical 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".