ACCEPTABILITY AND PRELIMINARY EFFICACY OF BE EPIC-VR TRAINING ON FRONTLINE HEALTH CARE WORKERS
Bibliographic record
Abstract
Abstract Be EPIC-VR is a person-centered communication (PCC) training program designed for healthcare providers working in dementia care. It is the first virtual reality (VR) program to use conversational artificial intelligence to train users to communicate with avatars depicting persons living with dementia (PLWD). The current study examined the acceptability and preliminary efficacy of Be EPIC-VR training. Participants included eight personal support workers from four home care and long-term care settings. Focus groups were conducted both immediately after VR sessions and after completing the Be EPIC-VR training program. Data analyses used framework analysis. The theme, relevant training supporting learning, reflected the acceptability of Be EPIC-VR. Be EPIC-VR’s innovative design facilitated significant learning gains, highlighting the benefits of experiential design, accessibility of training components, relevance regardless of career level, and group feedback on learning outcomes. The theme supporting preliminary efficacy was applying newly learned knowledge and skills with PLWD. Four subthemes emerged that mapped onto Be EPIC-VR’s foci. First, participants used the cues from the environment when interacting with PLWD. Second, participants, including those with English as a second language, reported applying PCC strategies which helped in understanding PLWD’s needs and addressing care refusal. Third, they noted an increase in self-efficacy in dementia care, which strengthened relationships with PLWD. Finally, they reported incorporating the preferences of PLWD during care interactions. Be EPIC-VR training emerges as a promising tool for enhancing skills of personal support workers, suggesting that immersive, VR-based training programs can foster empathetic, knowledgeable, and person-centered care approaches.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".