Using virtual care interventions to provide person-centred care to hospitalised older people with dementia
Bibliographic record
Abstract
BACKGROUND: Being in an unfamiliar environment away from family can exacerbate emotional stress in hospitalised older people with dementia. Technology solutions can be used to address their mental and emotional health needs. AIM: To generate greater understanding of technology adoption and to test strategies supporting virtual care interventions in hospitalised older people with dementia, such as the use of an iPad to connect them with their family members. METHOD: Older people with dementia in two Canadian hospitals were observed and interviewed to explore their experiences of using an iPad. Focus groups were conducted with staff and interviews were undertaken with two frontline nurses and three research partners with lived experience of dementia in hospitalised older people. Data were thematically analysed in collaboration with 12 stakeholders. Strategies to overcome the barriers identified were tested as part of the study. FINDINGS: There were three main barriers to implementing virtual care interventions: lack of familiarity with the technology; difficulties with operating the device; and privacy and connectivity issues. Strategies to overcome these barriers included providing personalised support, working with users to support adaptation, and ensuring privacy and optimal connectivity. CONCLUSION: Using an iPad has the potential to enable hospitalised older people with dementia to connect with their family members and take part in activities that support person-centred care. This is particularly important in times, such as the COVID-19 pandemic, when restrictions to hospital visits lead to social isolation.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".