USING VIRTUAL REALITY IN AGED CARE SETTINGS: A SCOPING REVIEW
Bibliographic record
Abstract
Abstract Recent advancement of virtual reality (VR) technology has led to growing interest in using VR in aged-care settings. VR can help ameliorate experiences of loneliness and social isolation, which is especially important during the COVID-19 pandemic. As a result, increasingly more studies are being published on this topic, and a comprehensive review of studies examining the facilitators and barriers of adopting VR in these settings is needed. This scoping review reports the facilitators and barriers to implementing VR in care settings among older adults, as well as the impact on social engagement and/or loneliness. We followed the Joanna Briggs Institute scoping review methodology, and searched the following databases: CINHAL, Embase, Medline, PsycInfo, Scopus, and Web of Science. Inclusion criteria includes articles published in the last five years that focus on older adults using VR in aged-care settings. 199 articles were retrieved and 21 articles were included in our review. Most of the articles (38%) originated from Australia. Key facilitators for using VR in aged care settings are the technology being user-friendly, comfortable, and easy to clean. Barriers included: technology issues (e.g., internet connectivity), staff attitude/ worries, and impact on residents, and structural considerations (e.g., lack of staff and time to assist with the VR program). VR technology can decrease loneliness and feelings of isolation, and provide opportunities to engage with others. Our review of the current evidence offers insights and recommendations for health care professionals to use VR technology in aged care settings, maximizing benefits and minimizing risks among users.
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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".