FACILITATORS AND BARRIERS TO IMPLEMENTING A VIRTUAL REALITY PROGRAM IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract To successfully implement virtual reality (VR) programs for long-term care (LTC) residents, it is essential to consider contextual factors. However, current research does not explore the LTC staff’s perspectives on implementing VR in their workplaces. This qualitative study aimed to fill this gap by exploring the facilitators and barriers to adopting VR in LTC, guided by the Consolidated Framework for Implementation Research (CFIR). We applied a Collaborative Action Research (CAR) approach, which involved three phases: (1) Reflect and Plan, (2) Act and Adapt, and (3) Evaluate. Ten focus groups were conducted with 20 staff in two Canadian long-term care homes. Thematic analysis was performed collectively with the team, including researchers, trainees, and patient and family partners. Our findings suggest that implementing a VR program in LTC requires readiness and capacity for implementation within the care home. Key factors that enabled implementation were staff champions, perceived benefits, and ease of use of the equipment. However, there were also barriers, such as limited resources, including Internet infrastructure, limited adaptability to meet local needs, and relative priority and staff workload. To overcome these barriers, our results indicate a need for organizational support for infrastructure and human resources. In addition, future research can evaluate the potential impact of facilitating residents’ VR sessions on staff’s job satisfaction and the involvement of residents’ families/caregivers as well as volunteers during the sessions to reduce staff hesitancy and workload.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".