Alternate Level of Care: Challenges and barriers for those who wait the longest
Bibliographic record
Abstract
Each year numerous Albertans are admitted to a hospital but upon discharge are no longer able to return home, despite efforts to provide adequate support. Of these individuals who require continuing care services, some will have care needs exceeding the services that are able to be provided in available LTC beds. When the appropriate continuing care services are not available individuals admitted to acute care hospitals (but no longer requiring their services) continue to occupy a bed and are classified as requiring an “alternate level of care” or ALC. This creates inefficiencies in the utilization of acute care resources, has multiple impacts across the entire healthcare system, and most importantly prohibits patients from receiving the appropriate care in the appropriate setting. In Alberta, between 2012 and 2015, there were an average of 2,706,571 hospital bed days per year; ALC days accounted for 10% of all hospital days in 2012-13, increasing to 12.2% in 2014-15. For the Calgary zone specifically, 15.2% of all 2014-15 hospital days were classified as ALC days this translates into approximately 330,201 Alberta hospital days classified as ALC in 2014-15. This is a stark contrast from previous years; from 2006- 08 ALC days accounted for only 2.2% of all hospital days. This represents an increase of 10 percentage points, or five times as many ALC days in 2014-15 compared to 2006-2008.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".