Using telepresence robots to support family virtual visits during the COVID‐19 Pandemic
Bibliographic record
Abstract
Abstract Background The COVID‐19 pandemic has disproportionately impacted older adults living with dementia in Long‐Term Care (LTC). Social isolation and loss of connections with families among residents have been detrimental and severely impacted quality of life. Method This project aims to enhance LTC homes' capacity to support virtual family visits using a telepresence robot. Research question: Is it feasible to implement robotic‐assisted virtual care in LTC homes? We applied a Collaborative Action Research (CAR) approach to work with stakeholders (frontline leaders, staff, patient and family partners) to explore the experiences of virtual family visits in four Canadian LTC homes. Guided by the Consolidated Framework of Implementation Research (CFIR), we conducted an online survey, interviews, focus groups, and observations to explore implementation experience. Results Our analysis identified three themes: (a) Relative advantage: Easy to visit, (b) Capacity for change: Readiness and organizational support, (c) Cultural safety for robot adoption: Champions leading the way during challenging times. Conclusion Our preliminary results suggest staff, residents, and families appreciated the robot for easy connection. Future research should apply inclusive methods to bring relevant stakeholders together to fully explore user experiences ‐ who is affected in what ways and the benefits, risks, and burdens of emerging technologies.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".