Health Care
Bibliographic record
Abstract
Patient and caregiver engagement (PCE) in health research and more broadly in health care delivery is increasingly recognized as foundational to achieving relevant health gains and health system improvements and is important to funders. Optimal ways to foster meaningful PCE, build capacity, and evaluate its implementation and achievements in health research require further exploration. In the Aging, Community and Health Research Unit, a variety of recruitment, co-design and analysis methods were used for PCE across 4 studies that examined quality of life and care for older adults with multiple chronic conditions and their caregivers. PCE occurred within different study designs (i.e., pragmatic trials, prospective cohort, literature review), requiring common and divergent engagement approaches. Strategies included engaging partners in: patient advisory councils, community advisory boards, steering committees, panel discussions, and policy forums. Information from partners on PCE was collected by interviews, focus groups, surveys, document analysis, website stories, photographs, usability testing, feedback, think aloud, needs assessment and gap identification, and persona-scenarios. Results showed the following strategies enhanced meaningful PCE and minimized burden: a) dedicated leads to facilitate relationship building, identify assets, needs, and preferences; b) flexibility in engagement mechanisms (e.g., ability to participate in-person or virtually); c) distributive governance models that foster strategic PCE in research stages, ensures a breadth of perspectives, and limits burden; and d) contingency strategies (e.g., back-up PCE representatives). Future research is warranted to identify the best methods to enable meaningful engagement, evaluate impact, and balance the risks and benefits of PCE amongst this vulnerable population.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.455 | 0.187 |
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".