Facilitators and barriers to codesigning social robots with older adults with dementia: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Social robots including telepresence robots have emerged as potential support in dementia care. However, the effectiveness of these robots hinges significantly on their design and utility. These elements are often best understood by their end-users. Codesign involves collaborating directly with the end-users of a product during its development process. Engaging people with dementia in the design of social robots ensures that the products cater to their unique requirements, preferences, challenges, and needs. The objective of this scoping review is to understand the facilitators, barriers, and strategies in codesigning social robots with older adults with dementia. METHODS AND ANALYSIS: The scoping review will follow the Joanna Briggs Institute scoping review methodology and will be conducted from November 2023 to April 2024. The steps of search strategy will involve identifying keywords and index terms from CINAHL and PubMed, completing search using identified keywords and index terms across selected databases (Medline, CINAHL, PubMed, AgeLine, Web of Science, PsycINFO, Scopus, IEEE, and Google Scholar), and hand-searching the reference lists from chosen literature for additional literature. The grey literature will be searched using Google. Three research assistants will screen the titles and abstracts independently by referring to the inclusion criteria. Three researchers will independently assess the full text of literature following to the inclusion criteria. The data will be presented in a table with narratives that answers the questions of the scoping review. ETHICS AND DISSEMINATION: This scoping review does not require ethics approval because it collects data from publicly available resources. The findings will offer insights to inform future research and development of robots through collaboration with older people with dementia. In addition, the scoping review results will be disseminated through conference presentations and an open-access publication in a peer-reviewed journal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.134 | 0.099 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.066 | 0.016 |
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".