A REFLECTION OF A STUDENT-LED VIRTUAL REALITY ADOPTION PROGRAM FOR PEOPLE LIVING WITH DEMENTIA IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract Previous research suggests that virtual reality (VR) holds promise in improving residents’ well-being in long-term care (LTC). With ongoing staff shortages, there is a critical need to explore, evaluate and share learnings on innovative approaches for adopting technologies, including VR, in LTC. In our research study conducted at two LTC homes in Vancouver, Canada, undergraduate students supported staff members in VR adoption. This study reflects on the opportunities and challenges of VR adoption from the students’ perspectives. Utilizing a critical reflection framework, our interdisciplinary team engaged in extensive discussions and reflections via Zoom meetings, resulting in the following five themes: (1) Challenging the stigma towards dementia in LTC, (2) Addressing ageism in older adults’ technology usage, (3) Building relationships through consistent and repeated visits to facilitate technology adoption, (4) Recognizing students’ value in technology implementation and their potential impact on LTC’s future workforce, and (5) Recognizing the importance of emotional and organizational support. Based on these reflections, our team devised the “ENRICH” framework, offering six practical tips to support student involvement in enhancing technology adoption in LTC. The team’s insights highlight the potential of engaging university students in promoting technology adoption in LTC settings. The “ENRICH” framework provides a practical approach to fostering technology uptake while offering valuable learning experiences for students. This study encourages further exploration of student participation in LTC research to advance future initiatives in this field.
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.
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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".