Facilitators and barriers to using telepresence robots in aged care settings: A scoping review
Bibliographic record
Abstract
Social isolation has been a significant issue in aged care settings, particularly during the COVID-19 pandemic, and is associated with adverse outcomes, including loneliness, depression, and cognitive decline. While robotic assistance may help mitigate social isolation, it would be helpful to know how to adopt technology in aged care. This scoping review aims to explore facilitators and barriers to the implementation of telepresence robots in aged care settings. Following the Joanna Briggs Institute scoping review methodology and the PRISMA extension for scoping reviews reporting guidelines, we searched relevant peer-reviewed studies through eight databases: CINAHL, MEDLINE, Cochrane, PsychINFO (EBSCO), Web of Science, ProQuest Dissertations and Theses Global, IEEE Xplore, and ACM Digital Library. Google was used to search gray literature, including descriptive, evaluative, quantitative, and qualitative designs. Eligibility includes: studies with people aged 65 years and older who interacted with a telepresence robot in a care setting, and articles written in English. We conducted a thematic analysis to summarize the evidence based on the constructs in the Consolidated Framework of Implementation Research. Of 1183 articles retrieved, 13 were included in the final review. The analysis yielded three themes: relative advantages, perceived risks and problems, and contextual considerations. The key facilitators to telepresence robot adoption are as follows: a feeling of physical presence, ease of use, mobility, and training. The barriers to implementation are as follows: cost, privacy issues, internet connectivity, and workflow. Future research should investigate the role of leadership support in implementation and practical strategies to overcome barriers to technology adoption in aged care settings.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".