IMPLEMENTING EPIC-VR IN HEALTH CARE: ALIGNING VIRTUAL REALITY TRAINING WITH ORGANIZATIONAL CONTEXT AND READINESS
Bibliographic record
Abstract
Abstract Be EPIC-VR is a virtual reality (VR)-based person-centered communication training for frontline healthcare workers in dementia care. The current study investigated factors influencing the successful implementation of Be EPIC-VR in home care and long-term care, using the Consolidated Framework for Implementation Research. Participants included eight managers from four care settings. Managers were chosen for their decision-making roles in enabling their teams to take Be EPIC-VR. Semi-structured interviews were conducted before and after Be EPIC-VR’s implementation and analyzed using framework analysis. Two themes emerged: organizational context and organizational readiness. Organizational context had two subthemes: increased training needs and staffing resources. Managers identified dementia-specific training needs evolving from limited training during the COVID-19 pandemic. Staffing-related resources, such as scheduling and backfilling, were essential for implementation. This organizational context shaped the organizational readiness for Be EPIC-VR, which was evidenced by three subthemes: relative priority for Be EPIC-VR, relative advantage of Be EPIC-VR, and tailoring strategies for successful implementation. Managers prioritized Be EPIC-VR’s implementation because it aligned with organizational goals to support communication and address responsive behaviours. Managers reported on the relative advantage of Be EPIC-VR compared with existing training programs, emphasizing its interactive VR simulations, the ability to practice new skills in a safe environment, and personalized feedback. Tailored strategies affecting implementation included having sufficient resources to support remote delivery, piloting with small groups, and promoting an openness to VR technology. The findings underscore the value of aligning innovations with organizational goals and tailoring implementation plans to specific organizational contexts.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".