Working with a Robot in Hospital and Long-Term Care Homes : Staff experience
Bibliographic record
Abstract
<title>Abstract</title> Although there is a growing literature on the use of telepresence robots in institutional dementia care settings, limited research focused on the perspectives of frontline staff members who deliver dementia care. Our objective was to understand staff perspectives on using telepresence robots to support residents with dementia and their families. Guided by the Consolidated Framework for Implementation Research, we conducted four focus groups and 11 semi-structured interviews across four long-term care (LTC) homes and one hospital in Canada. We included 21 interdisciplinary staff members (e.g., registered nurses, social workers, occupational therapists, recreational therapists) to understand their experiences with telepresence robots. Thematic analysis identified three key themes: 1) Staff Training and Support; 2) Robot Features; 3) Environmental dynamics for Implementation. Our results underscore the imperative of structural support at micro-, meso- and macro-levels for staff in dementia care settings to effectively implement technology. This study contributes to future research and practice by elucidating factors facilitating staff involvement in technology research, integrating staff voices into technology implementation planning, and devising strategies to provide structural support to staff, care teams, and care homes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".