FACILITATORS AND BARRIERS TO IMPLEMENTING A VIRTUAL REALITY PROGRAM IN LONG-TERM CARE
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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