Ethical considerations in implementing virtual reality programs in long-term care settings: Case studies from in Canada and the Czech Republic
Bibliographic record
Abstract
Virtual Reality (VR) presents opportunities for improving the quality of life of older adults living in long-term care (LTC) homes. While current research primarily examines the feasibility of VR implementation, there remains a lack of studies addressing the ethical considerations pertinent to older adults residing in care settings. Drawing upon case studies from LTC settings in Canada and the Czech Republic, this paper compares common challenges and unique ethical issues associated with VR implementation. We employ a human rights-based approach to discuss lessons learned in the two countries and implications for further research and development of VR interventions for LTC residents. Our reflection focuses on lessons learnt: 1) LTC residents have restricted access to benefits from VR in LTC, 2) risk aversion culture in LTC, 3) involvement of LTC residents in VR development and adoption, 4) cultural relevance, 5) ageism and exclusion, and 6) respecting the right to decline VR. The reflection underscores the importance of engaging relevant partners (residents, families, care partners, leadership teams, industrial partners, and researchers) to develop implementation plans and create collective ownership of the virtual reality program. Continuous team reflections on the design process, technology uptake, and implementation are crucial in ensuring residents’ well-being, equity, and cultural sensitivity in adopting technology in LTC. Informed by the reflection, we developed six practical strategies focusing on Access, Balance, Connection, Diversity, Engagement and Freedom to say no, acronymized as ABCDEF. Future research should explore system support, policies, and guidelines to support the ethical use of virtual reality in LTC 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 | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.018 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".