Multimodal Framework for Therapeutic Consultations
Bibliographic record
Abstract
Therapeutic engagement between client and clinician is a key indicator in determining treatment outcomes for clients with mental health disorders. Quantifying this type of engagement provides an opportunity for the development of an engagement quantification framework for therapeutic efficacy, based on a number of data streams including, body movement and synchronicity, speech, and gestures to determine an individual's level of engagement. In this paper, we present a subset of such a framework through the quantification of engagement based on Facial Affect Recognition, Head Motion, and Natural Language Processing. We propose the use of semantic analysis, emotion dynamics and transitions, and head motion to describe a participant's attention over the consultation. For emotion dynamics and transitions we employ seven standard categorical emotions; for head motion we use acute and chronic head movement; and for semantic analysis we employ Robustly Optimized BERT Pretraining Approach. These features derive two engagement levels: low and high. We performed experiments on the AnnoMI dataset, which contains 133 therapeutic consultation videos for low and high quality motivational interviews, and compared the resulting engagement to the level of motivational interviewing. We achieved an 89.1% average accuracy for the Clinician model and an 81.1% average accuracy for the Client model using Gradient Boost as a classifier.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".