Moving towards the Age-friendly hospital. A paradigm shift for the hospital-based care of the elderly.
Bibliographic record
Abstract
Care of the older adult in the acute care hospital is becoming more challenging. Patients 65-years and older account for 35% of hospital discharges and 45% of hospital days. Up to one-third of the hospitalized frail elderly loses independent functioning in one or more activities of daily living as a result of the ‘hostile environment’ that is present in the acute hospitals. A critical deficit of health care workers with expertise and experience in the care of the elderly also jeopardizes successful care delivery in the acute hospital setting.We propose a paradigm shift in the culture and practice of event-driven acute hospital-based care of the elderly which we call the Age-Friendly Hospital concept. Guiding principles include: a favorable physical environment; zero tolerance for ageism throughout the organization; an integrated process to develop comprehensive services using the geriatric approach; assistance with appropriateness decision-making and fostering links between the hospital and the community.Summary The Age-Friendly Hospital concept we propose may lead the way to enable hospitals in the fast-moving health care system to deliver high quality care without jeopardizing risk-benefit, function, and quality of life balances for the frail elderly.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".