Outside in: A Protocol for Qualitative Study of the Implementation of Immersive Technology in Dementia Care
Bibliographic record
Abstract
Older adults living in hospitals and long-term care (LTC) settings often lack opportunities for meaningful social engagement and interaction with the outside world, particularly due to mobility disabilities and frailty. Immersive virtual reality (VR) technologies have shown potential to overcome these barriers by virtually connecting individuals with nature, familiar environments, and cultural experiences, thereby promoting well-being and social engagement. However, existing research primarily utilizes head-mounted displays (HMDs) and involves healthy community-dwelling participants, limiting relevance for people living with dementia in care settings such as hospitals and LTC. This qualitative study protocol outlines the use of a non-HMD immersive technology tailored for people living with dementia in hospital and LTC settings. Employing wall projections and motion sensors, the intervention is specifically designed for accessibility among people living with dementia in hospitals and LTC environments. Guided by the Collaborative Action Research (CAR) approach, the study comprises three phases: Plan, Implement, and Evaluate. Focus groups, interviews, and ethnographic observations will be conducted to collect data. Thematic analysis will identify key themes related to feasibility, acceptability, and user experiences. Findings will inform the equitable design and implementation of future immersive technologies in dementia 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 | 0.078 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".