What does engagement mean to you? Exploring the experiences of the League older adult advisory group for social robot technology research
Bibliographic record
Abstract
Abstract Background Co‐creation methods are increasingly being used in the research and development of technologies that support older adults living with dementia and their care partners to live well. Use of collaborative methods to engage with the dementia community helps to ensure that research processes and end solutions are sensitively designed, reflective of needs and values, and responsive to priorities. Engagement also has proximal benefits for older adults: Being involved in purposeful activity has been shown to positively impact health and wellbeing outcomes. However, despite these benefits, the value of co‐creation methods in dementia research remains under‐investigated from the perspectives of older adults themselves. Here we seek to address this gap in the context of social robot research exploring the impact and value that older adults derive from engaging in dementia technology research. Method We carry out semi‐structed interviews with older adults who are members of a lived experience advisory group, known as the League. League members provide ongoing consultation on research activities carried out at the Neuroscience, Engagement and Smart Tech Lab. League members also complete surveys, including a subset of questions from the Patient Engagement In Research (PEIR) Framework, to capture quantitative responses on key components of engagement. We adopt a social‐technical perspective to generate a holistic appreciation for the impacts of engagement. Result Individual perspectives captured in this mixed methods study reveal that older adults have unique experiences with technology, and technology research, that modulate their level of engagement and the meanings that they derive from participation. Results demonstrate the impact that engagement has on biopsychosocial factors, roles, and identity, and underscore the ethical imperative of human‐centered research methods. Conclusion Insights will support the evidence base for person‐centred, co‐creation methodologies in dementia research, and showcase the potential for meaningful engagement to support the health and wellbeing of older adult collaborators.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".