Facilitators, barriers, and impacts to implementing dementia care training for staff in long-term care settings by using fully immersive virtual reality: a scoping review
Bibliographic record
Abstract
Background The increasing ageing population highlights the urgent need for enhanced dementia care training among formal caregivers. Virtual reality technology has emerged as an innovative tool to address this challenge, offering potential improvements in training outcomes. This scoping review focuses on identifying the barriers, facilitators, and impacts of implementing fully immersive VR training programs for dementia care among staff in long-term care facilities. Method The Consolidated Framework for Implementation Research informed our searching strategies and data analysis. Following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, this review included both published and unpublished studies. A systematic search of CINAHL, MEDLINE, Embase, Scopus, Web of Science, and ProQuest databases yielded 469 publications, with nine articles meeting the inclusion criteria. These studies, published in English between 2015 and 2024, involved 362 formal caregivers with a mean age ranging from 44.7 to 65 years. VR interventions were found to foster empathy (through first-person perspectives) and to help participants recognize triggers of responsive behaviors and apply solutions (via second-person and third-person perspectives). Results Most barriers and facilitators were associated with the innovation domain. The primary barriers included simulation sickness, uncomfortable headsets, and limited immersive, interactive, and embodied experiences. Key facilitators were technical advantages, highly immersive, interactive, and embodied experiences, a safe training environment, individual attributes, and the provision of orientation and support during training. The VR training programs demonstrated the potential to impact caregiving at multiple levels, including initial reactions, learning (knowledge, skills, and attitudes), behavioral changes, and broader systemic outcomes. Conclusion This scoping review maps out the current landscape of VR training for healthcare professionals. Future research should continuously improve the VR training experience by investigating the impact of VR training on dementia care outcomes, such as caregiver-resident interactions. By addressing the barriers and leveraging the facilitators, VR training can be successfully implemented to enhance the quality of care and wellbeing of residents living with dementia in long-term care homes.
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 | 0.032 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".