Facilitators and Barriers to Implementing VR in Dementia Care Training for Formal Caregivers: A Scoping Review
Bibliographic record
Abstract
Abstract As the aging population grows, the need for improved dementia care training for formal caregivers is urgent. Virtual reality (VR) offers a promising approach to enhance training outcomes. This scoping review examines the facilitators, barriers, and impacts of implementing fully immersive VR in dementia care training for formal caregivers in long-term care settings. Guided by the Consolidated Framework for Implementation Research, this review followed the Joanna Briggs Institute methodology and PRISMA-ScR guidelines. A systematic search of CINAHL, MEDLINE, Embase, Scopus, Web of Science, and ProQuest identified 469 publications, with nine meeting inclusion criteria. These studies, published between 2015 and 2024, involved 362 formal caregivers aged 44.7 to 65 years. VR interventions fostered empathy through first-person perspectives and helped participants recognize behavioral triggers and apply caregiving strategies using second- and third-person perspectives. Barriers and facilitators were primarily in the innovation domain. Barriers included simulation sickness, headset discomfort, and limited immersive, interactive, and embodied experiences. Facilitators included technological advantages, highly immersive and interactive experiences, a safe training environment, individual user attributes, and structured orientation and support during training. VR training demonstrated benefits across multiple levels, from initial reactions and learning (knowledge, skills, and attitudes) to behavioral changes and systemic outcomes. This review highlights the current landscape of VR-based dementia care training. Future research should refine VR experiences and assess their impact on caregiver-resident interactions. Addressing barriers and leveraging facilitators can support the effective implementation of VR training to enhance care quality and resident well-being in long-term care settings.
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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 | 0.036 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".