A qualitative study on virtual reality and psychosocial needs of residents living with dementia in long-term care
Bibliographic record
Abstract
Objectives: Virtual reality (VR) holds significant potential to address the psychosocial needs of residents living with dementia in long-term care (LTC) settings. This study employed a qualitative research approach, guided by Kitwood's person-centred care approach - personhood theory, to explore how VR can support the well-being of residents living with dementia. Methods: Conducted at three LTC homes in Vancouver, Canada, our team collected and analysed qualitative data through conversational interviews, observations, and feedback from 33 residents, 21 staff and three family members. Results: We identified six themes that illustrate how VR supports residents' well-being and psychosocial needs: (1) VR offers meaningful experiences; (2) VR promotes the sense of continuity of self and identity in the past; (3) VR fosters social connections in long-term care; (4) VR involves family in care; (5) VR provides comfort and a pleasant virtual environment; and (6) VR engages residents in later stages of dementia and diverse needs. VR enables residents to engage in activities beyond physical boundaries, evokes reminiscence, fosters social connections, and provides a sense of comfort. Conclusion: This study emphasizes the interactive components of VR content and the involvement of family members in VR sessions, leading to meaningful experiences that enhance the residents' comfort and attachment. Overall, VR shows promise as a tool for enhancing the psychosocial well-being of residents living with dementia in LTC, advocating for continued research and advancements 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 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 | 0.001 |
| 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".