Integrating Assistive Robots into Professional Caregivers’ Workflow in Aging Healthcare
Bibliographic record
Abstract
The increasing demands of an aging population have intensified the strain on caregiving resources. Assistive robots, such as Stretch, offer a promising way to ease caregiver workload and improve the quality of care. We evaluated the Stretch™ robot, a mobile manipulator designed for direct interaction with older adults and caregivers, using a mixed-methods approach in a home simulation environment. Six professional caregivers participated by completing surveys, interviews, and hands-on interaction sessions with the assistive robot, including tasks such as remote video calls and item delivery. Key areas assessed included trust, usability, workload, and technology familiarity. Results indicated a rise in trust toward the robot after the interactions, high usability scores, and reports of medium-to-low perceived workload. Caregivers highlighted Stretch’s effective object handling and its potential to assist with daily caregiving tasks. Although some participants were initially skeptical, their impressions became more positive after observing, interacting, and operating the robot. Overall, the findings suggest that assistive robots could be effectively integrated into caregiving workflows without adding to caregiver burden. Future work will focus on improving Stretch’s autonomous capabilities and refining the user interface to support broader adoption in real-world aging care environments.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 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".