Engaging older adults in healthcare research and planning: a realist synthesis
Bibliographic record
Abstract
The importance of citizen involvement in healthcare research and planning has been widely recognized. There is however, a lack of understanding of how best to engage older adults, Canada's fastest growing segment of the population and biggest users of the healthcare system. We aimed to address this gap by developing an understanding of the engagement of older adults and their caregivers in healthcare research and planning. We conducted a review of available knowledge on engagement in healthcare research and planning with a focus on older adults and their caregivers. A five stage engagement framework emerged from this study that can be used to guide engagement efforts. We are continuing to collaborate with older adults and decision makers to develop and test strategies based on the presented framework. Background The importance of engaging the community in healthcare research and planning has been widely recognized. Currently however, there is a limited focus on older adults, Canada’s fastest growing segment of the population and biggest users of the healthcare system. Objective This project aimed to develop an understanding of engagement of older adults and their caregivers in healthcare research and planning. Method A realist synthesis was conducted of the available knowledge on engagement in healthcare research and planning. The search methodology was informed by a framework for realist syntheses following five phases, including consultations with older adults. The synthesis included theoretical frameworks, and both peer-reviewed and grey literature. Results The search generated 15,683 articles, with 562 focusing on healthcare research and planning. The review lead to the development of a framework to engage older adults and their caregivers in healthcare research and planning. The 5 stages environment, plan, establish, build, and transition are accompanied with example context, mechanism, and outcomes to guide the use of this framework. Conclusion We have identified a framework that promotes meaningful engagement of older adults and their caregivers. We are continuing to collaborate with our community partners to further develop and evaluate engagement strategies that align with the presented framework.
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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 | 0.057 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".