THE IMPACT OF AND BARRIER TO USING THE SOCIAL ROBOT PARO IN CARE SETTINGS
Bibliographic record
Abstract
Given the complexity of providing dementia care in hospitals, integrating technology into practice is a high challenge and an important opportunity. Although there are growing demands and interest in using social robots in a variety of care settings to support dementia care, little is known about the impacts of the robotics and their application in care settings - what worked, in where and how. Following the PRISMA protocol, this scoping review describes the reported impacts of and barriers to using the robot PARO in care settings. Scientific electronic databases and relevant websites were searched between June and August 2018 to identify peer-reviewed publications and grey literature (including reports and other online resources) published in last two decades. A total of 29 papers were included in the review. Content analysis identified three key impacts and three barriers involved in the use of PARO. Main impacts include: reducing behavioral symptoms, improving social engagement, and enhancing comfort and quality of care experience. Key barriers to the use of the robot are cost, infection concerns, and social stigma. We identified three research gaps: (a) patients’ experiences and needs remain unexplored, (b) few studies investigate the process of how to use the robot effectively to meet clinical needs and (c) research on applying theory to understand the robot-human interaction is absent. In the past, intervention studies have been primarily researcher-focused rather than patient-oriented. Future research should focus more on knowledge translation to support technology adoption and ensure benefits to meet the needs of older adults.
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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 | 0.084 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".