A System‐Wide Analysis Using a Senior‐Friendly Hospital Framework Identifies Current Practices and Opportunities for Improvement in the Care of Hospitalized Older Adults
Bibliographic record
Abstract
Older adults are vulnerable to hospital-associated complications such as falls, pressure ulcers, functional decline, and delirium, which can contribute to prolonged hospital stay, readmission, and nursing home placement. These vulnerabilities are exacerbated when the hospital's practices, services, and physical environment are not sufficiently mindful of the complex, multidimensional needs of frail individuals. Several frameworks have emerged to help hospitals examine how organization-wide processes can be customized to avoid these complications. This article describes the application of one such framework-the Senior-Friendly Hospital (SFH) framework adopted in Ontario, Canada-which comprises five interrelated domains: organizational support, processes of care, emotional and behavioral environment, ethics in clinical care and research, and physical environment. This framework provided the blueprint for a self-assessment of all 155 adult hospitals across the province of Ontario. The system-wide analysis identified practice gaps and promising practices within each domain of the SFH framework. Taken together, these results informed 12 recommendations to support hospitals at all stages of development in becoming friendly to older adults. Priorities for system-wide action were identified, encouraging hospitals to implement or further develop their processes to better address hospital-acquired delirium and functional decline. These recommendations led to collaborative action across the province, including the development of an online toolkit and the identification of accountability indicators to support hospitals in quality improvement focusing on senior-friendly care.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".