Engaging older adults in the process of aging research: a multimethod study evaluating the experience and efficacy of a citizen advisory group for a dementia risk reduction program
Bibliographic record
Abstract
BACKGROUND: Collaborative research with end-users is an effective way to generate meaningful research applications and support greater impact on practice and knowledge exchange. To address these needs, a Citizen Advisory Group (CAG) of nine older adults (ages 64-80, 67% women) was formed to advise scientists on the development of Brain Health PRO (BHPro), a web-based platform designed to increase dementia prevention literacy and awareness. The current study evaluated if the CAG met its objectives, how inclusion of the CAG aligned with collaborative research approaches, and the CAG's experience and satisfaction throughout the development process. METHODS: An anonymous online survey was administered to the CAG members and 30 scientist/trainee authors of the BHPro chapters. The CAG also participated in an online focus group. RESULTS: Most CAG members and chapter authors agreed that the CAG met its primary objectives and added unique value to BHPro. Both groups viewed the CAG's involvement as well-aligned with engaged scholarship, co-production, integrated knowledge translation, and, to a lesser extent, participatory research practices. CAG members reported high satisfaction with personal goal attainment, which included learning, collaborating with others, and making a meaningful impact. Content analyses of the focus group revealed three categories: 1) personal benefits related to learning, connection, and feeling valued, 2) value of a masked peer-review process, and 3) an accessible final product. CONCLUSIONS: Findings suggest that collaborating with end-users in the process of aging research confers personal and scientific benefits for both older adults and researchers.
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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 | 0.068 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".