Immersive Intergroup Contact: Using Virtual Reality to Enhance Empathy and Reduce Stigma Towards Schizophrenia
Bibliographic record
Abstract
Stigma towards individuals with schizophrenia reduces quality of life, creating a barrier to accessing education and employment opportunities. Schizophrenia is one of the most stigmatized mental health conditions, and stigma is prevalent particularly among healthcare professionals. In this study, we investigated whether Virtual Reality (VR) can be incorporated into interventions to reduce stigma. In particular, we compared the effectiveness of three VR conditions based on intergroup contact theory in reducing stigma in form of implicit and explicit attitudes, and behavioral intentions. Through an immersive virtual consultation in a clinical setting, participants (N=60) experienced one of three different conditions: the Doctor's perspective (embodiment in a majority group member during contact), the Patient's perspective (embodiment in a minority group member) and a Third-person perspective (vicarious contact). Results demonstrated an increase of stigma on certain explicit measures (perceived recovery and social restriction) but also an increase of empathy (perspective-taking, empathic concern) across all conditions regardless of perspective. More importantly, participants' viewpoint influenced the desire for social distance differently depending on the perspective: the Third-person observation significantly increased the desire for social distance, Doctor embodiment marginally decreased it, while Patient embodiment showed no significant change. No change was found in the Implicit Association Test. These findings suggest that VR intergroup contact can effectively reduce certain dimensions of stigma toward schizophrenia, but the type of perspective experienced significantly impacts outcomes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".