Co-Designing a Virtual Reality Application to Enhance Reminiscence Therapy for Persons with Dementia
Bibliographic record
Abstract
Abstract Reminiscence therapy for persons with dementia is often being conducted by employing analog media including pictures and videos organized and presented by a caregiver. However, such media lacks the immersive experience to support patient engagement and successful recollection of reminiscence events. Recently, Virtual Reality (VR) is gaining momentum as a potential technological tool to support dementia care due to its increased immersion, presence, and embodiment. Haptic artifacts can be used to enrich reminiscence therapy as part of the multi-sensory stimuli to increase immersion and patient engagement, as well as improving social connectedness and cognitive health. The purpose of this project is to explore the use of VR application to advance reminiscence therapy for persons with dementia. We have prototyped an immersive and non-immersive VR framework that allows caregivers to deliver reminiscence therapy for persons with dementia with varying stages in their disease progression. These reminiscence therapy sessions are built by employing a narrative storyboard and content management through a series of co-designing sessions with content experts at the Geriatric Dementia Unit in Ontario, Canada. A caregiver-led VR framework will be adopted to enable the caregiver to guide the persons with dementia to safely navigate through the interactive VR environment, while allowing the patients to engage with the interactive VR elements using a point-and-pinch gesture approach. We anticipate that the VR experiences hold the potential for improving the interactions between persons with dementia and caregivers, as well as enhancing the reminiscence experiences to promote the maximal therapeutic benefit of patient’s recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".