Implementation of a Virtual Reality recreation program in long-term care
Bibliographic record
Abstract
Introduction: This manuscript describes the implementation of a Virtual Reality (VR) recreation program at long-term care sites across Ontario, Canada, using the RE-AIM Framework to guide the implementation and its evaluation. Methods: We developed a VR recreation program to enhance the lives of long-term care residents, through 3 sequential phases. In Phase 1, we learned about resident and staff needs through focus groups, staff surveys and observations. In Phase 2, we developed 10 VR experiences, based on the data from Phase 1. In Phase 3, we implemented the VR experiences and supporting manual and measured their implementation, using the RE-AIM Framework. Results: We found the VR program to be highly (but not consistently) implementable across all sites. Factors that supported implementation were the following: resident interest in the content and technology, relative ease of use for staff to implement and formally integrating VR into the recreation calendar. Factors that impeded implementation were the following: the size of the headset, inability for the headset to cast given the sites' Information Technology infrastructure and some content that was not engaging. Conclusions: VR programs are highly implementable and this implementation is enhanced by integration of the program into existing recreational systems, ease of use and resident engagement.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".