Evaluating Human-Robot Interactions to Support Healthy Aging-in-Place
Bibliographic record
Abstract
Declines in older adults' cognitive and physical health pose challenges to maintaining their independence. Robots can improve independent living and facilitate aging-in-place. Despite recent innovations in healthcare robotics, the use of robots has not advanced significantly among older adults. This review seeks to understand human-robot interactions in older adults, focusing on their experiences and perceptions of robots for independent living. We identified 17 studies that utilized qualitative methods to investigate older adults and/or their caregivers' experiences and perceptions of robots designed to help older adults improve independent living. Drawing on content analysis, we identified eight themes: usefulness, ease of use, safety, reliability, self-efficacy, satisfaction, emotional connection with the robot and reciprocity, and intention to use. The findings provide insights to improve existing robots and guide future research about designing robots with higher acceptance. This review may have implications for policymakers, practitioners, and researchers working with robotics to support healthy aging-in-place.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".