Digital travel using virtual reality in inpatient psychiatric care: Focus group exploration of perspectives from individuals with lived experience.
Bibliographic record
Abstract
OBJECTIVE: Hospitalization in psychiatry is a challenging experience associated with increased levels of distress, anxiety, and loneliness. Novel technologies are being developed to help alleviate these symptoms and support the treatment and rehabilitation of these individuals. This study aims to explore the perspectives of individuals with lived experience of a complex mood disorder on the proposal of an immersive virtual reality (VR) travel-in-nature application with a social feature being an available service in a psychiatric inpatient unit. METHODS: A thematic analysis was performed with data acquired from two focus group semistructured interviews conducted by a patient partner with individuals currently hospitalized in a short-term inpatient unit dedicated to complex mood disorders. RESULTS: Three themes were generated from the thematic analysis: (a) factors enhancing acceptability, (b) barriers, and (c) envisioning the future of the application and VR in inpatient mental health. CONCLUSIONS: Participants were largely positive regarding the potential of the application and VR in psychiatric inpatient care. They viewed it as a promising rehabilitation tool for relaxation and positive escapism. Concerns regarding suitability, potential risks associated with the technology, and technical barriers were raised and warrant further investigation. IMPLICATIONS FOR PRACTICE: This study's preliminary findings offer relevant information for designing the implementation process of VR in psychiatric inpatient units, with the intent of tailoring services to the needs and realities of their intended users. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".