ORGANIZATIONAL READINESS FOR A VIRTUAL REALITY TRAINING PROGRAM CALLED BE EPIC-VR
Bibliographic record
Abstract
Abstract An essential first step to implementing virtual reality programming in home care and long-term care settings is to assess organizational readiness by determining factors that enhance the likelihood of its successful implementation. Be EPIC-VR is one such virtual reality program that supports dementia-specific, person-centered communication training for frontline healthcare workers. Guided by the Consolidated Framework for Implementation Research (CFIR), the current study aimed to identify factors influencing Be EPIC-VR’s implementation in home care and long-term care settings. Semi-structured interviews were conducted with nine managers from home care and long-term care settings in Canada. Transcripts from these interviews were analyzed using the Framework Analysis’ five-step ongoing, iterative process: familiarization, identifying a thematic framework, indexing, charting, and mapping/interpretation. Textual data were open-coded and organized deductively (using CFIR’s pre-set codes) and inductively (for emergent codes) into themes and subthemes. Four themes emerged as factors contributing to organizational readiness including 1) openness to virtual reality as a training tool, 2) staffing and training logistics, 3) organizational culture supporting staff development, and 4) external pressures for organizational sustainability. These findings will guide how the Be EPIC-VR implementation team collaborates with organizational decision makers to ensure that Be EPIC-VR is a good fit for those organizations, to prepare for its implementation, and to optimize the likelihood of success. The study findings offer valuable insights for researchers and practitioners working to implement new virtual reality interventions in home care and long-term care settings.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".