Exploring the Potential for Use of Virtual Reality Technology in the Treatment of Severe Mental Illness Among Adults in Mid-Norway: Collaborative Research Between Clinicians and Researchers
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) technology is not currently used in the treatment of severe mental health illness in Norway. OBJECTIVE: We aimed to explore the potential of VR as a treatment for severe mental health illness in Norway, through collaborative research between clinicians and researchers. METHODS: A collaborative research team was established, comprising researchers, the manager at a district psychiatric center, and the manager of the local municipal mental health service. An all-day workshop with eight clinicians-four from specialist mental health services and four from municipal mental health services-was conducted. The clinicians watched three different VR movies and after each one, they answered predefined questions designed to reflect their immediate thoughts about VR's potential use in clinical practice. At the end of the workshop, two focus group interviews, each with four clinicians from each service level, were conducted. RESULTS: VR technology in specialist services might be a new tool for the treatment of severe mental health illness. In municipal mental health services, VR might particularly be useful in systematic social training that would otherwise take a very long time to complete. CONCLUSIONS: We found substantial potential for the use of VR in the treatment of severe mental health illness in specialist and municipal mental health services. One of the uses of VR technology with the greatest potential was helping individuals who had isolated themselves and needed training in social skills and everyday activity to enable them to have more active social lives. VR could also be used to simulate severe mental illness to provide a better understanding of how the person with severe mental illness experiences their situation.
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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 | 0.063 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".