The staff perspectives of facilitators and barriers to implementing virtual reality for people living with dementia in long-term care
Bibliographic record
Abstract
Introduction: One emerging technology in long-term care (LTC) is virtual reality (VR), an innovative tool that uses head-mounted devices to provide the viewer with an immersive experience. It has been shown that VR has a positive impact on the well-being of residents living with dementia, and staff are essential in the implementation and sustainable use of technology. Currently, there is a lack of inclusion and focus on direct staff perspectives on VR implementation in LTC. This paper aims to report staff perspectives on VR adoption in a 2-year study on a virtual reality program at three Canadian LTC homes. Methods: Our interdisciplinary team (clinicians, people living with dementia and family partners, trainees, and researchers) explored the facilitators and barriers to implementing VR in LTC, guided by the Consolidated Framework for Implementation Research (CFIR) and intersectionality supplemented CFIR. Twenty-one participants were recruited, including recreation staff, care aides, nurses, screeners, and leadership team members. The team collected data through staff interviews, focus groups, and ethnographic observation field notes. Reflexive thematic analysis was performed to identify themes reporting the facilitators and barriers for VR implementation in LTC from staff perspectives. Results: The data analysis resulted in three facilitators and four barriers. Facilitators are (1) perceived VR benefits, (2) integrate VR into workflow and routines, and (3) partner with skillful VR champions. Barriers include (1) staff concerns about VR use, (2) financial burden and competing priorities, (3) lack of infrastructure and physical spaces, and (4) staff workload and limited leadership support. Discussion: This study contributes to the field with staff perspectives on facilitators and barriers to VR implementation. It underscores the rarely discussed aspects of VR implementation, such as funding prioritization and implementation timing. We offer practical strategies to inform future practices and research. Future studies should further explore long-term VR implementation, the involvement of family members as VR facilitators, and the use of VR in LTC.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".