CIHI Survey: Alternatives to Acute Care?
Bibliographic record
Abstract
In fter treatment in acute care hospitals is completed, many patients need follow-up or ongoing health services in settings other than in an acute care facility.Those services include those provided in continuing-care institutions (such as rehabilitation centres, long-term-care facilities and nursing homes), through home-care programs and by patients' families.However, alternatives to acute care are not always readily available for patients who require them and this leads to extended stays in acute care facilities.Patients occupying acute care beds when they are well enough to be cared for elsewhere are identified as alternative level of care (ALC) patients in some hospitals.Patients experiencing ALC hospital stays do so for a variety of reasons.In a recent survey of hospital executives from five countries, limited availability of post-acute care was identified as one of the factors leading to discharge delays from acute care facilities ( Blendonet al. 2004).According to some Ontario-based reports, other reasons for ALC hospital stays include inadequate communication and coordination among care providers, lack of hospital discharge-planning protocols and deficient utilization management of post-acute services (Ontario District Health Council Archives 2002; Bruce 2002).Here we look at the characteristics of the patients experiencing alternative level of care stays in hospital in 2004-2005.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".