Influence of Virtual Reality on Negative Symptoms and Quality of Life of Patients With Schizophrenia: A Meta-Analysis: Influence de la réalité virtuelle sur les symptômes négatifs et la qualité de vie des patients atteints de schizophrénie : une méta-analyse
Bibliographic record
Abstract
Background Schizophrenia brings about diverse challenges to patients, their families and society. It is up to the healthcare system to effectively resolve these concerns and benefit all involved parties. With the emerging development and adaptation of virtual reality (VR), this may offer a new direction and potential for treating people with schizophrenia. Our goal was to employ meta-analysis to evaluate the influence of VR on the clinical outcomes and quality of life in people with schizophrenia. Methods We performed an extensive screening of randomized controlled trials (RCTs) examining the effect of VR on the clinical outcomes in people with schizophrenia. Our search included the scientific databases PubMed, Embase, Web of Science and the Cochrane Library, and included RCTs from the date of database establishment till 1 June 2023 and we followed a strict study of inclusion and exclusion criteria. The meta-analysis was conducted in RevMan 5.4. Results We selected 963 patients from 10 RCTs. Relative to other forms of interventions, VR therapy considerably alleviated overall clinical (SMD = −4.33, 95% CI = [−6.92, −1.74], P = 0.001) and negative symptomology (SMD = −1.38, 95% CI = [−2.46, −0.30], P = 0.01) among in people with schizophrenia. In contrast, no significant improvements were observed in positive symptoms or quality of life among these patients. Further subgroup analyses of the results indicated that there were differences in the improvement of negative symptoms among patients across the different interventions ( P = 0.01). Conclusions Based on our meta-analysis, VR-based treatment regimen significantly improves overall and negative symptoms in people with schizophrenia. Further exploration is warranted to elucidate the influence of VR on patient positive symptoms and quality of life.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.052 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".