Staff’s Attitudes towards the Use of Mobile Telepresence Robots in Long-Term Care Homes in Canada
Bibliographic record
Abstract
This cross-sectional study investigated staff's attitudes towards the use of mobile telepresence robots in long-term care (LTC) homes in western Canada. We drew on a Health Technology Assessment Core Model 3.0 to design a survey examining attitudes towards nine domains of mobile telepresence robots. Staff, including nurses, care staff, and managers, from two LTC homes were invited to participate. Statistical analysis of survey data from 181 participants revealed that overall, participants showed positive attitudes towards features and characteristics, self-efficacy on technology use, organizational aspects, clinical effectiveness, and residents and social aspects; neutral attitudes towards residents' ability to use technology, and costs; and negative attitudes towards safety and privacy. Participants who disclosed their demographic backgrounds tended to exhibit more positive attitudes than participants who did not. Content analysis of textual data identified specific concerns and benefits of using the robots. We discuss options for implementing mobile telepresence robots in LTC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".