BE EPIC-VR: TRANSLATING AN IN-PERSON, PERSON-CENTERED COMMUNICATION TRAINING PROGRAM INTO VIRTUAL REALITY
Bibliographic record
Abstract
Abstract Be EPIC is a dementia-specific, person-centered communication training program for frontline healthcare workers that uses simulations with trained actors, reflection, and feedback. Scaling Be EPIC is limited by resource and methodology demands of recruiting and training actors and consistency of delivery across sites. Virtual reality (VR) is a promising solution to the problem of scaling Be EPIC because it provides interactive, realistic, and consistent simulations. However, there is limited research on implementing VR to train frontline healthcare workers. Guided by the Consolidated Framework for Implementation Research, the current study used an effectiveness-implementation hybrid design to compare simulation experiences of Be EPIC-VR with Be EPIC-in person and real-world clinical encounters. The study also explored factors that can influence the future implementation of Be EPIC-VR in long-term care home settings during the pre-implementation period. Six frontline healthcare workers who completed the Be EPIC in-person version with trained actors also completed the same assessment simulation in VR, followed by an interview. Thematic analyses revealed two themes that contributed to the simulation’s realism: the immersive nature of the virtual environment and the accuracy of the avatar’s visual, verbal, and behavioral characteristics. Moreover, the factors influencing the successful implementation of VR simulations in long-term care home settings include sufficient technological infrastructure and workplace personnel who can assist with Be EPIC-VR implementation. The findings highlight the potential to scale Be EPIC using VR and provides insights into the iterative process involved in translating an in-person training into a VR training program.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".