Engaging older adults in health care research and policy: Guidelines from the CHOICE project
Bibliographic record
Abstract
Introduction: Engaging the community in health care research and planning has been recognized as an important component of system improvement (1). The input and involvement of older persons is particularly critical, given that older adults are high users of the health care system, but are often excluded from health research studies. Unfortunately, guidelines for how to engage older adults in these initiatives are not readily available in the literature. Aim: Guidelines for engaging older adults and their families in health care research and policy will be presented, based on the CHOICE (Choosing Healthcare Options by Involving Canada’s Elderly) knowledge synthesis project. Methods: In the CHOICE project, we conducted a realist synthesis (2-3) of available knowledge on strategies for engagement of older adults and their families (including other informal caregivers) in health care. The search methodology was informed by a framework for realist syntheses (4) as well as Arksey and O'Malley’s (5) design considerations for scoping reviews. Our synthesis encompassed theoretical frameworks and peer-reviewed and grey literature. Expert consultation included interviews with academics (n=5), two focus group interviews with seniors and families, and two half-day workshops organized with our partner Patients Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.411 | 0.342 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.024 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.012 | 0.021 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".