Increasing quality of life for older adults living in collective dwellings using virtual reality: a feasibility study
Bibliographic record
Abstract
The number of older adults living in collective dwellings is increasing. It is important to research effective strategies to maintain and enhance quality of life for older adults living in collective dwellings. Meaningful leisure, such as the ability to travel, is associated with increases in quality of life for older adults. Unfortunately, many older adults, especially those living in collective dwellings, face barriers to travel. Virtual reality (VR) may help older adults living in collective dwellings overcome barriers to travel. The present study examined whether older adults living in collective dwellings tolerated and enjoyed immersive VR, and whether six weeks of virtual tourism affected their quality of life, social engagement, and loneliness. Fourteen older adults living in retirement homes in Toronto participated in this study. Results suggested that participants tolerated immersive VR without experiencing cybersickness, and that they were happier, more excited, and less anxious immediately following VR exposure. Levels of social engagement increased following the six-week virtual tourism program. These quantitative findings were further supported by qualitative interviews. No changes in quality of life or loneliness were found. Limitations include a lack of a control group and small sample size. Addressing these limitations will help to isolate the effects of the virtual tourism program on indices of well-being.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".