Immersive Virtual Reality Simulation for Suicide Risk Assessment Training: Innovations in Mental Health Nursing Education
Bibliographic record
Abstract
Background The complexity of psychiatric patient presentations requires the standardization of nursing education through simulation-based education to ensure essential skills development. Technological advances like virtual reality offer an innovative opportunity to enhance simulation-based nursing education. Our study aimed to improve nursing education by examining the impact of an immersive virtual reality simulation (iVRS)-based education program regarding suicide risk assessment (SRA) training on the educational outcomes, learner experience, and user satisfaction of nursing students, with comparison to a nonimmersive computer desktop version. Methods Two VR SRA case scenarios were developed depicting virtual patients with acutely and chronically elevated suicide risk. These simulations were created in two formats: an iVRS (n = 52) that used a VR headset and handheld controllers, and a computer desktop virtual reality simulation (dVRS, n=187). Results iVRS and dVRS had comparable improvements regarding educational outcomes, user engagement and overall user experience for SRA training. Conclusions The benefit of iVRS compared to other simulation modalities in psychiatric nursing education may vary depending on the type of content being taught.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".