A Descriptive Analysis of Emotional Expressions and Variations Across Avatar Therapy
Bibliographic record
Abstract
Avatar Therapy (AT) is a promising intervention for individuals with treatment-resistant schizophrenia (TRS). This study examined how often (and what types of) emotions were expressed by patients and their Avatars during immersive AT sessions. Verbatims and audio recordings from 18 TRS participants were analyzed, and emotional expressions including Anger, Contempt/Disgust, Fear, Sadness, Shame/Embarrassment, Interest, Surprise, and Joy were coded. Mann-Whitney U tests and Spearman’s rho correlations were used to compare emotional profiles between good and non-responders. Results showed a weak but significant negative correlation for Sadness among non-responders and a moderate significant correlation for Joy among good responders. Overall, emotions were similarly expressed across all participants. As the first exploratory study to assess emotional variation in immersive AT dialogues, these findings suggest that emotional content may relate to treatment response and could help identify potential predictors of therapeutic outcomes.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".