FEASIBILITY AND ACCEPTABILITY OF IMPLEMENTING AN AI-ENABLED SERVICE ROBOT IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract In long-term care (LTC), staff members are responsible for the complex needs of people with disabilities. This task can be enhanced by integrating service robots equipped with Artificial Intelligence (AI). These robots offer personalized service for residents and empower the staff with efficient support. Despite growing interest, the implementation of service robots in LTC remains under-investigated. This study examines the feasibility and acceptability of implementing a service robot, named Aether, in a Canadian LTC home, guided by the underpinnings of Collaborative Action Research. We included interdisciplinary staff in pre- and post-intervention focus groups and conducted conversational interviews with residents, staff members, and managers. Consolidated Framework for Implementation Research (CFIR) informed our implementation, data collection and analysis. We identified key facilitators (staff engagement and training) and barriers (environmental dynamics and resource limitations). Our results underscore the imperative of structural support at micro-, meso- and macro-levels for staff in LTC to implement technology effectively. This study contributes valuable insights into the future development and deployment of AI-enabled service robots in LTC.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".