Feasibility and Acceptability of Deploying a Collaborative Service Robot in Long-Term Care : Staff Experiences
Bibliographic record
Abstract
In Long-Term Care (LTC), staff members are responsible for addressing residents’ complex needs. Emerging research suggests integrating Artificial Intelligence (AI)-enabled service robots can enhance staff care delivery. We aim to explore the feasibility of deploying such robots in staff’s care practice, which remains under-explored. Guided by the underpinning principles of Collaborative Action Research, we deployed an AI-enabled robot, Aether, in a group home in Canada. We included care staff and a care home manager in deployment and post-intervention focus groups to understand their experiences of having Aether in their nursing practice and care delivery. Consolidated Framework for Implementation Research informed our data collection and thematic analysis. We identified facilitators and barriers in three interconnected themes: (1) Robot Features, (2) Environmental Dynamics, and (3) Training and Staff Engagement. Implementing Aether in a care home is feasible with sufficient support to staff. Our study highlighted the imperative need for (1) structural support at individual, organizational, and macro levels for care teams using AI-enabled innovation and (2) fostering partnerships to overcome barriers and support the sustainable deployment of such innovation.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".