Healthcare for the Aging Citizen and the Aging Citizen for Healthcare: Involving Patient Advisors in Elder-Friendly Care Improvement
Bibliographic record
Abstract
With an aging population and a healthcare system that is overly reliant on providing expensive and sometimes problematic hospital-based care for older Canadians, driving improvements that promote elder-friendly care has never been more critical. The Acute Care for Elders (ACE) Strategy at Toronto's Mount Sinai Hospital is the focus of a pan-Canadian collaborative delivered by the Canadian Foundation for Healthcare Improvement in partnership with the Canadian Frailty Network. The intent is to spread the ACE Strategy's elder-friendly models of care and practices to 18 participating healthcare delivery organizations. A key element of the ACE Collaborative is the inclusion of patient advisors as members of the 18 teams. This article considers the development of elder-friendly care models and practices, with lessons for patient advisors and organizations on the necessary skill-mix, as well as lessons for providers and managers on ways to more effectively engage patient advisors in health system improvement to better serve an aging population.
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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 | 0.038 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".