IMPLEMENTING EPIC-VR IN HEALTH CARE: ALIGNING VIRTUAL REALITY TRAINING WITH ORGANIZATIONAL CONTEXT AND READINESS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it