A New Method to Better Measure and Interpret Non-Verbal Communication in Patient-Therapist Interactions
Bibliographic record
Abstract
Abstract Objective(s): To develop a novel method of examining facial affects in psychotherapeutic settings and interpreting the subliminal microexpressions and emotions they represent.Results: Therapy sessions are videotaped, and verbal content is evaluated with computer assistance from ATLAS.ti. Visual content is screened for facial action units/micro-expressions using the Facial Action Coding System (FACS) and reevaluated using the Emotional Facial Action Coding System (EmFACS) in order to discern the underlying emotions. Working Alliance Inventory scores are compiled to assess their impact on the quality of the therapeutic relationship. Reliability of facial action unit coding is ensured by a training course and an independently evaluated standardized test at Innsbruck University. Interrater reliability was excellent (Cohen’s κ > 0.80). Application of this method on a small study population to measure non-verbal communication and affects demonstrated its feasibility and usability. We believe this methodology to be widely implementable. Its application in psychotherapy may provide greater insights in investigations into important aspects of therapeutic interaction (e.g. verbal/non-verbal communication between clinicians and patients in psychiatric contexts, quality of psychotherapeutic relationships, etc.), and may be exploitable in clinical psychotherapeutic settings to better understand mechanisms of change in therapy.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".