Determining the severity and prevalence of cybersickness in virtual reality simulations in psychiatry
Bibliographic record
Abstract
Abstract Background The rise in virtual reality (VR) applications in healthcare has introduced immersive VR simulations as a valuable training tool for medical professionals. Despite its advantages, VR use can induce cybersickness, characterized by symptoms such as nausea and disorientation. This study examines the relationship between cybersickness and the degree of physical movement in VR simulations used for psychiatric education. Methods The study involved two VR simulations offered at a Canadian mental health hospital: an opioid overdose response (OO) (high movement VR) and suicide risk assessment (SRA) (low movement VR). Participants’ experiences were measured using the Simulator Sickness Questionnaire (SSQ) before and after the training sessions. A nonparametric Mann–Whitney U-test was conducted to compare SSQ scores between the two VR simulations. Results A total of 91 participants, including healthcare practitioners and students, were involved. The mean SSQ score for the OO training was 4.59/48 (SD = 5.78), while for the SRA, it was 3.10/48 (SD = 3.48). Mann–Whitney U-test revealed a significant increase in nausea scores in OO simulation compared to SRA simulation (p = 0.0275), with higher nausea reported in the OO simulation. No significant increases were found in oculomotor symptoms. Conclusions Participants in the OO training experienced higher levels of nausea compared to those in the SRA simulation, likely due to increased need for physical movement. These findings underscore the importance of considering the degree of physical movement in the VR training design, specifically the educational value of these movements and the risk of cybersickness negatively impacting VR tolerability for learners.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".