Exploration of the role of emotional expression of treatment-resistant schizophrenia patients having followed virtual reality therapy: a content analysis
Bibliographic record
Abstract
BACKGROUND: Emotional responses are an important component of psychotherapeutic processes. Avatar therapy (AT) is a virtual reality-based therapy currently being developed and studied for patients suffering from treatment resistant schizophrenia. Considering the importance of identifying emotions in therapeutical processes and their impact on the therapeutic outcome, an exploration of such emotions is needed. METHODS: The aim of this study is to identify the underlying emotions at the core of the patient-Avatar interaction during AT by content analysis of immersive sessions transcripts and audio recordings. A content analysis of AT transcripts and audio recordings using iterative categorization was conducted for 16 patients suffering from TRS who underwent AT between 2017 and 2022 (128 transcripts and 128 audio recordings). An iterative categorization technique was conducted to identify the different emotions expressed by the patient and the Avatar during the immersive sessions. RESULTS: The following emotions were identified in this study: Anger, Contempt/ Disgust, Fear, Sadness, Shame/ Embarrassment, Interest, Surprise, Joy and Neutral. Patients expressed mostly neutral, joy and anger emotions whereas the Avatar expressed predominantly interest, disgust/contempt, and neutral emotions. CONCLUSIONS: This study portrays a first qualitative insight on the emotions that are expressed in AT and serves as a steppingstone for further investigation in the role of emotions in the therapeutic outcomes of 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 | 0.003 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".