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Record W4362153655 · doi:10.1177/23337214231166355

Facilitators and Barriers to Using Virtual Reality and its Impact on Social Engagement in Aged Care Settings: A Scoping Review

2023· review· en· W4362153655 on OpenAlexafffund
Lillian Hung, Jim Mann, Christine Wallsworth, Mona Upreti, Winnie Kan, Alisha Temirova, Karen Lok Yi Wong, Lily Haopu Ren, Flora To‐Miles, Joey Wong, Caitlin Lee, David Kar Lai So, Sonia Hardern

Bibliographic record

VenueGerontology and Geriatric Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsVancouver Coastal HealthUniversity of British Columbia
FundersVictoria General Hospital FoundationVancouver Coastal Health Research InstituteMitacs
KeywordsLonelinessPsycINFOScopusSocial isolationMEDLINEPsychologyIsolation (microbiology)Virtual realityNursingApplied psychologyMedicineGerontologyMedical educationComputer scienceSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Aim: This scoping review aims to identify the facilitators and barriers to the implementation of VR technology in the aged-care setting. Background: Virtual reality (VR) offers the potential to reduce social isolation and loneliness through increased social engagement in aged-care settings. Methods and Analysis: This scoping review followed the Joanna Briggs Institute scoping review methodology and took place between March and August 2022. The review included a three-step search strategy: (1) identifying keywords from CINHAL, Embase, Medline, PsycInfo, Scopus, and Web of Science (2) conducting a second search using all identified keywords and index terms across selected databases; and (3) searching the reference lists of all included articles and reports for additional studies. Results: The final review included 22 articles. The analysis identified factors affecting the VR technology implementation in aged care settings to reduce isolation and loneliness: (a) key facilitators are local champions and staff training. (b) barriers include technological adaptability, video quality, and organizational culture. Conclusion: Existing evidence points to VR as a promising intervention to decrease loneliness and feelings of isolation and improve social engagement in older people living in aged-care settings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.605
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.112
GPT teacher head0.450
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations47
Published2023
Admission routes2
Has abstractyes

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