Exploring Therapeutic Outcomes Through Dyadic Interactions: The Role of Patient-Avatar Dynamics in Avatar Therapy
Bibliographic record
Abstract
OBJECTIVE: Despite the efficacy of current therapies, a significant proportion of patients with schizophrenia, a complex mental disorder marked by both positive (present) and negative (absent) symptoms, are considered to have treatment-resistant schizophrenia. Avatar therapy (AT) allows patients to interact with a three-dimensional representation of their most distressing voices in a virtual reality setting. The therapy shows promise in reducing impairments and improving quality of life through the establishment of a therapeutic alliance and the exploration of dyadic interactions (verbal exchanges) between patients and their avatar. The purpose of this study was to investigate differences in dyadic interactions throughout the immersive sessions of AT and to clarify the relationship between these interactions and therapeutic success by analyzing dyads as predictive indicators of positive outcomes in AT. METHODS: Mean frequencies for the 10 most prevalent dyads identified in previous AT research were reported for 35 patients. A logistic regression model was implemented, and these dyads were used to predict variances in Psychotic Symptom Rating Scales-auditory hallucination scores 1 month after the completion of AT. RESULTS: Variances in mean frequencies were reported for the dyads. A positive relation between the avatar (provocation)-patient (self-affirmation) dyad and the therapeutic outcome was found to be significant (OR=2.29, p=0.049). CONCLUSIONS: This research is pioneering in its in-depth examination of therapeutic interactions in AT, with a particular focus on dyadic interactions. Future studies should prioritize the quality rather than quantity of these interactions to more accurately forecast their effects on potential indicators of positive outcomes in AT.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".