CO-CREATING A VIRTUAL REALITY PROGRAM WITH PEOPLE LIVING WITH DEMENTIA IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract People living with dementia in long-term care (LTC) are a diverse group with varied physical and cognitive abilities and backgrounds. Previous research has shown that virtual reality experiences can bring joy and foster the health and well-being of people living with dementia. A tailored approach based on cultural preferences, needs, abilities, disabilities, and contextual limitations is needed to foster practical implementation, uptake, and sustainability. This qualitative study harnessed the experiential knowledge of people with dementia, care partners and frontline staff to co-build a novel Virtual Reality Program in two Canadian LTC homes. We conducted focus groups and interviews with 10 residents living with dementia, 10 family care partners and 12 staff to explore their preferences on the video content and delivery methods. We also explored participants’ experiences in the co-creation process. Our interdisciplinary team, including people with dementia and family partners, researchers, clinicians, and trainees, analyzed the data thematically. We identified four themes: 1) Significance of culturally relevant and diverse videos to address heterogeneous population, 2) Acknowledgement of residents’ autonomy and choice in the co-creation process, 3) Feelings of satisfaction and motivation through contributing and learning, and 4) Appreciation of a respectful co-design environment. The results offer useful insights to inform future directions in co-designing safe and accessible virtual programs with people living with dementia in LTC research.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".