Impact of a Virtual Reality Intervention on Stigma, Empathy, and Attitudes Toward Patients With Psychotic Disorders Among Mental Health Care Professionals: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Previous studies have found that psychotic disorders are among the most stigmatized mental disorders. Of note, virtual reality (VR) interventions have been associated with improvements in attitudes and empathy and reduced stigma toward individuals with psychotic disorders, especially among undergraduates, but this has not been examined among mental health care professionals. OBJECTIVE: We aimed to evaluate the effectiveness of a newly developed VR intervention for mental health care professionals to improve attitudes and empathy and reduce stigma toward people with psychotic disorders. METHODS: We conducted a randomized controlled trial and recruited eligible mental health care professionals from a tertiary mental health care institution. Both arms (VR intervention and VR control groups) were evaluated at baseline, postintervention, and 1-month follow up. The evaluation included outcomes related to attitudes (modified attitudes toward people with schizophrenia scale), stigma (social distance scale, personal stigma scale), and empathy (empathetic concern subscale of the Interpersonal Reactivity Index). The experience with the VR intervention was assessed using a user satisfaction questionnaire, and qualitative feedback was gathered. RESULTS: Overall, 180 mental health care professionals participated and completed the study. Both groups showed improvements in attitude, social distance, and stigma scores but not the empathy score following the intervention. The VR intervention group had better user satisfaction than the VR control group. In addition, certain outcome measures were positively associated with specific factors including female gender, higher education level, certain job roles, years of work, and presence of loved ones with a mental disorder. CONCLUSIONS: Both the intervention and control VR groups of mental health care professionals showed improvements in attitudes, stigma, and social distance toward people with psychotic disorders. Future longitudinal studies may want to evaluate the impact of VR on caregivers and the public on these same and other outcome measures to reduce stigma and improve empathy toward individuals with psychotic disorders. TRIAL REGISTRATION: clinicaltrials.gov NCT05982548; https://clinicaltrials.gov/study/NCT05982548.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".