Expectations of a virtual reality program for older adults with dementia in hospitals: Perspectives of patient partners, families, staff, and care leaders
Bibliographic record
Abstract
Background: Emerging evidence suggests that virtual reality (VR) technology can potentially improve the wellbeing of older adults living with dementia in hospital care units. Nevertheless, older patients are often excluded from VR opportunities. Meaningful engagement of patient partners, family caregivers, staff, and care leaders is needed to ensure the appropriate development and implementation of these programs. Objective: To understand the expectations of patient partners, families, staff, and care leaders regarding development and implementation of a VR program for patients with dementia in hospitals. Method: Drawing on principles of Collaborative Action Research and underpinned by the Person-Centred Framework, we conducted qualitative focus groups and interviews with 42 individuals including 7 patients, 9 family members, 17 frontline staff members and 9 organizational leaders. Results: We performed a thematic analysis and identified three interconnected themes: (1) anticipating positive functions and outcomes, (2) considerations on VR program implementation, and (3) desired VR features for patients with dementia. Conclusion: This study explored multiple partners' needs and priorities on a VR program in hospitals, emphasizing the pivotal role of multipartner collaboration and supportive care environment essential for delivering a person-centred VR experience. Future studies are recommended to further the investigation by deploying and evaluating a person-centred VR program for patients with dementia in hospital settings through collaboration with multiple partners.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".