Facilitators and barriers to using virtual reality and augmented reality and its impact on social engagement in aged care settings: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Increasingly more studies are being conducted on the use of virtual reality (VR) and augmented reality (AR) in aged care settings. These technologies can decrease experiences of loneliness which is especially important during the COVID-19 pandemic. With the growing interest in using VR/AR in care settings among older adults, a comprehensive review of studies examining the facilitators and barriers of adopting VR/AR in these settings is needed. This scoping review will focus on facilitators and barriers related to VR/AR in care settings among older adults, as well as the impact on social engagement and/or loneliness. METHODS AND ANALYSIS: We will follow the Joanna Briggs Institute scoping review methodology. We will search the following databases: CINHAL, Embase, Medline, PsycINFO, Scopus and Web of Science. Additional articles will be handpicked from reference lists of included articles. Inclusion criteria includes articles that focus on older adults using VR or AR in aged care settings. Our team (which includes patient and family partners, an academic nurse researcher, a clinical lead and trainees) will be involved in the search, review and analysis process. ETHICS AND DISSEMINATION: We will be collecting data from publicly available articles for this scoping review, so ethics approval is not required. By providing a comprehensive overview of the current evidence on the strategies, facilitators, and barriers of using VR/AR in aged care settings, findings will offer insights and recommendations for future research and practice to better implement VR/AR. The results of this scoping review will be shared through conference presentations and an open-access publication in a peer-reviewed journal.
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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 | 0.100 | 0.081 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.060 | 0.012 |
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".