CO-CREATING A VIRTUAL REALITY IN HOSPITAL WITH PATIENT AND FAMILY PARTNERS AND STAFF FOR OLDER ADULTS WITH DEMENTIA
Bibliographic record
Abstract
Abstract Growing evidence suggests that Virtual Reality (VR) is promising to improve the wellbeing of older patients in dementia care units in hospitals. However, older patients are often excluded from VR opportunities. Engaging patient partners, family caregivers and staff in co-creating VR program shows potential in addressing unique needs of older patients, supporting staff in implementation, and enhancing understanding complexity in clinical settings. However, literature that describes fulsome partnership with abovementioned joint stakeholders in the co-creation process is absent. The study aims to understand psychosocial needs of older adults with dementia in hospital and how VR could be best implemented in the complex clinical setting. Drawing principles of Collaborative Action Research (CAR) and applying an equity and inclusive lens, we conducted qualitative focus groups, co-design workshops and interviews with 46 stakeholders (7 patient partners, 8 family caregivers, 19 staff members and 12 leaders) in hospital. Consolidated Framework for Implementation Research (CFIR) informed our data collection and analysis. We identified three key themes to co-create a VR program for older adults with dementia in hospital to address their psychosocial needs and facilitate staff’s implementation, acronymized as aim 1) Approach matters; 2) Interactiveness; and 3) Multi-sensory stimulation. Our results underscore the imperative of engaging joint stakeholders in co-creation for older adults with dementia in hospital to address their psychosocial needs with VR, who deserve digital equity but are traditionally underrepresented in technology program development and implementation. This study contributes valuable insights into the future development and deployment of VR in geriatric care settings.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".