Exploring the Benefits of Virtual Reality-Assisted Therapy Following Cognitive-Behavioral Therapy for Auditory Hallucinations in Patients with Treatment-Resistant Schizophrenia: A Proof of Concept
Bibliographic record
Abstract
BACKGROUND: Combining cognitive behavioral therapy (CBT) for psychosis with another psychosocial intervention comprising virtual reality (VR)-assisted therapy (VRT) may improve targeted outcomes in treatment-resistant schizophrenia patients. METHODS: Ten participants having followed CBT were part of our comparative clinical trial comparing VRT to CBT and were selected at the end of the study as they desired to continue to achieve improvements with VRT (CBT + VRT). Clinical assessments were administered before/after treatments and at follow-ups. Changes in outcomes were examined using linear mixed-effects models. To gain a more in depth understanding on CBT + VRT, therapists' notes, and open interviews on a sub-group of patients were qualitatively analyzed. RESULTS: Findings showed that the sequence of both interventions was appreciated by all patients. Several significant improvements were found throughout time points on auditory verbal hallucinations, beliefs about voices, depressive symptoms, symptoms of schizophrenia and quality of life. Although most of these improvements were in similar range to those observed in our comparative trial, effects of CBT + VRT on depressive symptoms and symptoms of schizophrenia were larger than those found for either intervention alone. CONCLUSION: This proof of concept is the first to merge gold-standard CBT with VRT for treatment refractory voices and to suggest a certain synergistic effect.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".