USING VIRTUAL REALITY IN LONG-TERM CARE TO REDUCE SOCIAL ISOLATION
Bibliographic record
Abstract
Abstract Virtual Reality (VR) has become increasingly accessible for older adults, providing opportunities for interventions that address loneliness and social isolation in long-term care. However, the effectiveness of VR programs can be influenced by various factors, such as the backgrounds, preferences, and capacities of the target population. This qualitative study investigates the acceptability and feasibility of a recreational VR program for social engagement in two Canadian long-term care homes since January 2023. The study involved 20 residents (with various levels of cognitive and physical impairments) who participated in weekly VR group sessions facilitated by staff. Ethnographic observation and video-recorded conversational interviews were conducted with residents during the VR sessions. We also conducted 10 focus groups with 20 staff members. Four patient partners were involved as co-researchers in the team. We performed the thematic analysis with patient partners. We identified three themes: (1) storytelling builds residents’ sense of self, (2) positive emotions persist even when the video is forgotten, and (3) VR empowers resident-resident and staff-resident connections. The findings demonstrate that using VR in long-term care settings is feasible and acceptable for older adults with different cognitive and physical impairments. VR programs have the potential to enhance social engagement and support residents’ personhood as a meaningful activity by improving inclusion, social engagement, comfort, and recognition of their identity. Future research could explore the long-term impact of VR experiences in addressing social isolation and loneliness among older adults in long-term care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".