Use of Acute Care Hospitals by Long-Stay Patients: Who, How Much, and Why?
Bibliographic record
Abstract
The effects of long-term hospitalizations can be severe, especially among older adults. In Manitoba, between fiscal years 1991/1992 and 1999/2000, 40 per cent of acute care hospital days were used by the 5 per cent of patients who had long stays, defined as stays of more than 30 days. These proportions were remarkably stable, despite major changes in the bed supply. Approximately two-thirds of long-stay patients were aged 75 or older. Medical record review for a small sample of long-stay medical patients aged 75 or older revealed that 42 per cent of the days spent in hospital were spent either awaiting transfer to another level of care (home care, nursing home, or chronic care), or were due to in-hospital factors, such as awaiting consults, tests, or treatments. Hospital information systems and early discharge planning may help to alleviate lengthy discharge delays and result in better care for these patients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".