Engagement of older adults with multimorbidity as patient research partners: Lessons from a patient-oriented research program
Bibliographic record
Abstract
BACKGROUND: Patient "engagement" in health research broadly refers to including people with lived experience in the research process. Although previous reviews have systematically summarized approaches to engaging older adults and their caregivers in health research, there is currently little guidance on how to meaningfully engage older adults with multimorbidity as research partners. OBJECTIVES: This paper describes the lessons learned from a patient-oriented research program, the Aging, Community and Health Research Unit (ACHRU), on how to engage older adults with multimorbidity as research partners. Over the past 7-years, over 40 older adults from across Canada have been involved in 17 ACHRU projects as patient research partners. METHODS: We developed this list of lessons learned through iterative consensus building with ACHRU researchers and patient partners. We then met to collectively identify and summarize the reported successes, challenges and lessons learned from the experience of engaging older adults with multimorbidity as research partners. RESULTS: ACHRU researchers reported engaging older adult partners across many phases of the research process. Five challenges and lessons learned were identified: 1) actively finding patient partners who reflect the diversity of older adults with multimorbidity, 2) developing strong working relationships with patient partners, 3) providing education and support for both patient partners and researchers, 4) using flexible approaches for engaging patients, and 5) securing adequate resources to enable meaningful engagement. CONCLUSION: The lessons learned through this work may provide guidance to researchers on how to facilitate meaningful engagement of this vulnerable and understudied subgroup in the patient engagement literature.
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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 | 0.222 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.007 | 0.040 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".