Effect of a Virtual Reality Contact-Based Educational Intervention on the Public Stigma of Depression: Randomized Controlled Pilot Study
Bibliographic record
Abstract
BACKGROUND: Public stigma against depression contributes to low employment rates among individuals with depression. Contact-based educational (CBE) interventions have been shown to reduce this public stigma. OBJECTIVE: We investigated the ability of our Virtual Reality Antistigma (VRAS) app developed for CBE interventions to reduce the stigma of depression. METHODS: Sixteen medical students were recruited and randomized 1:1 to the intervention group, who used the VRAS app (VRAS group), and the control group, who watched a video on depression. The depression stigma score was assessed using the Depression Stigma Scale (DSS) and Attitudinal Social Distance (ASD) questionnaire at pre- and postintervention. Feasibility was assessed in both groups and usability was assessed only in the VRAS group after the intervention. A qualitative study was performed on the acquisition of knowledge about stigma in both groups based on participants' answers to open-ended questions and interviews after the intervention. RESULTS: The feasibility score was significantly higher in the VRAS group (mean 5.63, SD 0.74) than in the control group (mean 3.88, SD 1.73; P=.03). However, no significant differences were apparent between the VRAS and control groups for the DSS (VRAS: mean 35.13, SD 5.30; control: mean 35.38, SD 4.50; P=.92) or ASD (VRAS: mean 12.25, SD 3.33; control: mean 11.25, SD 1.91; P=.92). Stigma scores tended to decrease; however, the stigma-reducing effects of the VRAS app were not significant for the DSS (pre: mean 33.00, SD 4.44; post: mean 35.13, SD 5.30; P=.12) or ASD (pre: mean 13.25, SD 3.92; post: mean 12.25, SD 3.33; P=.12). Qualitative analysis suggested that the VRAS app facilitated perspective-taking and promoted empathy toward the patient. CONCLUSIONS: The CBE intervention using virtual reality technology (VRAS app) was as effective as the video intervention. The results of the qualitative study suggested that the virtual reality intervention was able to promote perspective-taking and empathy toward patients. TRIAL REGISTRATION: University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) UMIN000043020; https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000049109.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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) | 0.012 | 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".