Virtual reality adaptation of Be EPIC: pre-implementation studies of person-centered dementia care training
Bibliographic record
Abstract
Abstract Background and Objectives Person-centered communication is critical in dementia care, yet personal support workers (PSWs) often lack sufficient training, which can reduce care quality. Be EPIC is an in-person training that teaches learners to build person-centered communication skills using actor-based simulations. A virtual reality (VR) version of Be EPIC was developed to expand access and consistency. Two pre-implementation studies explored factors influencing Be EPIC-VR’s implementation by assessing readiness for VR training among managers and realism and usability from PSWs’ perspectives. Research Design and Methods Study 1 used the Consolidated Framework for Implementation Research (CFIR) to assess readiness for VR training through semi-structured interviews with managers of PSWs (n = 9) in long-term and home care settings. Interviews focused on CFIR’s innovation, inner setting, and outer setting domains. Guided by Fox’s taxonomy of VR research, Study 2 involved PSWs (n = 7) who completed a Be EPIC-VR simulation and were interviewed about its realism, usability compared to live actors, and implementation factors related to CFIR’s innovation and inner setting domains. Results Study 1 identified 4 themes: external pressures for organizational sustainability, organizational culture supporting staff development, staffing and training logistics, and openness to VR for training. In Study 2, PSWs described VR simulations as immersive and realistic, though some reported limited mobility and headset incompatibility. While managers expressed concerns about the use of VR technology, PSWs noted that clear onboarding and facilitator guidance ensured accessibility. Both groups confirmed sufficient structural resources for implementation. Discussion and Implications Successful implementation of VR-based training in dementia care depends on aligning implementation readiness, organizational culture, and logistical resources. Early end-user engagement and an iterative approach enabled continual refinement of Be EPIC-VR based on managers’ openness to VR and PSWs’ user experiences. The findings position VR training as a promising method to improve dementia care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".