Involving patients and families in a social robot study
Bibliographic record
Abstract
Innovative research in care practice for older people can benefit from the active involvement of patient and family partners. Involvement may begin with identifying priorities, then move to formulate research questions and to plan the research methods, to data collection, and finally to analysis and knowledge dissemination. However, in the field of dementia care, actively engaging patients and families in co-research is a novel practice that needs exploration. This paper describes the experiences and perspectives of two patient researchers and three family researchers, along with four clinicians (two physicians, a nurse, and an occupational therapist) within a social robot project in dementia care. Meeting notes, team reflection focus groups, follow–up interviews, and a research journal were used to document the research process. The results are presented in three themes: (a) identify challenges and lessons learned, (b) co-inquire enriched learning, (c) co-produce knowledge for care improvement. All team members agreed that an inclusive environment was important to facilitate meaningful partnerships for undertaking research together. Trust and respect were seen as vital for a rewarding and productive experience in the co-inquiry journey. Some of the challenges to sustaining participant engagement were competing priorities and a risk of tokenism. This article provides a rich description as well as practical details of the research experiences among team members. We offer examples of lessons learned and practical tips to help others increase the engagement of patients and families in research. Experience Framework This article is associated with the Innovation & Technology lens of The Beryl Institute Experience Framework. (http://bit.ly/ExperienceFramework) Access other PXJ articles related to this lens. Access other resources related to this lens
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".