The benefits of and barriers to using a social robot PARO in care settings: a scoping review
Bibliographic record
Abstract
BACKGROUND: Given the complexity of providing dementia care in hospitals, integrating technology into practice is a high challenge and an important opportunity. Although there are a growing demand 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, i.e., what worked, in which situations, and how. METHODS: Scientific databases and Google Scholar were searched to identify publications published since 2000. The inclusion criteria consisted of older people with dementia, care setting, and social robot PARO. RESULTS: A total of 29 papers were included in the review. Content analysis identified 3 key benefits of and 3 barriers to the use of PARO. Main benefits include: reducing negative emotion and behavioral symptoms, improving social engagement, and promoting positive mood and quality of care experience. Key barriers are: cost and workload, infection concerns, and stigma and ethical issues. This review reveals 3 research gaps: (a) the users' needs and experiences remain unexplored, (b) few studies investigate the process of how to use the robot effectively to meet clinical needs, and (c) theory should be used to guide implementation. CONCLUSIONS: Most interventions conducted have been primarily researcher-focused. Future research should pay more attention to the clinical needs of the patient population and develop strategies to overcome barriers to the adoption of PARO in order to maximize patient benefits.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".