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
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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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".