Engaging Older Adults in Health Care Decision-Making: A Realist Synthesis
Bibliographic record
Abstract
BACKGROUND: Engagement in healthcare decision making has been recognized as an important, and often lacking, aspect of care, especially in the care of older adults who are major users of the healthcare system. OBJECTIVE: We aimed to conduct a review of available knowledge on engagement in healthcare decision making with a focus on older patients and their caregivers. METHODS: We conducted a realist synthesis focusing on strategies for engagement of older patients and their caregivers in healthcare decision making. The synthesis encompassed theoretical frameworks and both peer-reviewed and grey literature. Expert consultations included interviews (n = 2) with academics and group consultations (n = 3) with older adults and their caregivers. Abstracts that reported description, assessment, or evaluation of strategies for engagement of adult patients, families, or caregivers (i.e., that report on actual experiences of engagement) were included. RESULTS: The search generated 15,683 articles, 663 of which were pertinent to healthcare decision making. Theoretical and empirical work identified a range of strategies and levels of engagement of older patients and their families in healthcare decision making. The importance of communication emerged as a key recommendation for meaningful engagement among providers and patients and their caregivers. The principles developed in this study should be implemented with consideration of the context in which care is being provided. CONCLUSIONS: We have developed a framework that promotes the engagement of patients and their caregivers as equal partners in healthcare decision making. Future research should implement and test the framework in various clinical settings.
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 | 0.082 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".