Facilitators and barriers to codesigning social robots with older adults living with dementia: A scoping review
Bibliographic record
Abstract
Background: Social robots are increasingly used to support older adults living with dementia by providing not only reminders and companionship but also emotional support, engagement through conversation, and a sense of comfort. Engaging older adults living with dementia in the codesign process ensures that their unique needs and challenges are met, thereby enhancing the relevance and usability of these technologies. However, limited evidence exists on effective engagement strategies for codesigning with older adults living with dementia. This scoping review aims to explore the facilitators and barriers to codesigning social robots with older adults living with dementia. Methods: Following the Joanna Briggs Institute scoping review methodology, we searched six databases including Medline, CINAHL, Web of Science, PsycINFO, IEEE Xplore, and Google Scholar (for grey literature). The inclusion criteria focused on studies involving older adults with dementia in the codesign of social robots. After screening 513 records, six studies met eligibility criteria. Data extraction covered study characteristics, codesign activities, and identified facilitators and barriers. Results: The analysis identified three facilitators: adapted methods for people with dementia, the application of theoretical frameworks to guide codesign, and the inclusion of caregivers in the codesign process. Identified barriers included: unclear roles and information, unfamiliar and uncomfortable environments, and a lack of diversity among participants. Conclusions: This scoping review underscores the need for evidence-based frameworks and inclusive strategies to support the meaningful involvement of older adults living with dementia in social robot codesign. Addressing identified barriers and leveraging facilitators can enhance engagement and ensure that social robots better align with the lived experiences and needs of this population. Future research should explore supportive approaches that promote a more collaborative and dementia-friendly design process.
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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 | 0.047 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".