ADVANCING HOSPITAL CARE FOR OLDER ADULTS: SCIENCE, POLICY, AND PRACTICE FROM FOUR GLOBAL PARTNERS
Bibliographic record
Abstract
For older adults, the benefits of hospital care are often compromised by the experience of hospitalization itself. The complexity of care for older adults increases the risk for adverse outcomes and complicates discharge. Furthermore, existing hospital design and practices – such as immobility, under-nutrition, sleep deprivation, and unfamiliar surroundings – may cause unintended but significant harm. Quality approaches designed to improve the care of older adults in hospital demonstrate improved physical function, lower rates of delirium, fewer discharges to long-term care, and improved satisfaction. Increasingly, research is converging on the recognition that adapting processes across the entire organization is needed to achieve these benefits consistently. This symposium will provide an overview of current knowledge on hospital-acquired disability. Three collaborators will then share their approaches, blending clinical research and implementation science, to develop large-scale programs to advance care for hospitalized older adults. In Ontario, Canada, a provincial Senior Friendly Hospital (SFH) strategy identified priorities for system-wide improvement, evolving into SFH ACTION – an 87-hospital collaborative engaged in quality improvement for senior-friendly care. In the Netherlands, a national Senior Friendly Hospital strategy coordinates development of hospital-broad approaches and directly engages community advisors in the improvement and appraisal process. In Queensland, Australia, a state-wide older person friendly survey has been completed, and the “Eat, Walk, Engage” program continues to spread across sub-acute and acute hospitals. The presenters will describe their unique approaches to a common challenge, and also ways in which they have bridged their geographic distances, finding opportunities to collaborate and share ideas.
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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 | 0.071 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".