Co-creating a Virtual Reality Program for Older Adults with Dementia in Hospital
Bibliographic record
Abstract
Abstract Virtual Reality (VR) is promising in improving the well-being of older adults with dementia in hospitals; however, traditional VR design and VR experience delivery to meet their diverse needs. Involving older adult patient partners, family caregivers, and staff in the co-design of gerontechnology is considered best practice, yet few researchers have adopted this inclusive approach. Appreciative Inquiry offers a collaborative framework to engage relevant users, leveraging their expertise and lived experiences for innovation. Our study aims to understand the contribution of Appreciative Inquiry in engaging multiple partners in co-creating a VR program for Older Adults with Dementia in hospitals. We co-created a VR program guided by Appreciative Inquiry principles, engaging patient partners, families, hospital staff and leaders. We adapted methods to facilitate meaningful participation, including six focus groups and interviews with 56 participants across two hospital units. Participants’ insights informed the iterative development and refinement of the VR program. Patient partners, families, staff, and leaders meaningfully contributed to the design process. The collaborative process fostered a sense of ownership among participants, challenged assumptions about dementia care, and promoted positive interactions and experiences within the project team. Our approach demonstrated that using tailored methods is essential for authentic engagement, ensuring the developed technology meets real-world needs and will more likely be adopted in care practice. The study demonstrates the value of Appreciative Inquiry-informed co-design partnerships in co-creating technology. Future studies should further explore co-design methods across diverse cultural and organizational contexts with diverse older adults and other 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| 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".