Regional variations of care in home care and long-term care: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Many aging adults undergo progressive loss of autonomy, develop increasingly complex medical needs and experience multiple care transitions. We sought to determine the degree of variation in rates of transfer from home care services and long-term care in several Canadian jurisdictions. METHODS: In this retrospective cohort study, we examined transitions from home care services and long-term care to different possible end states: change in health stability (getting better or worse), transfer to hospital, transfer to another care setting or death. We used standardized interRAI assessments from long-term care and home care linked to hospital records (data from the Discharge Abstract Database and National Ambulatory Care Reporting System) from 2010 to 2016. Multistate modelling was used to adjust for patients with complex health status and transitions in care. RESULTS: We report data for 254 664 patients in home care programs and 162 045 residents in long-term care. Compared with patients in Ontario, patients requiring home care services in Alberta and British Columbia had increased odds of being admitted to hospital regardless of the underlying severity of illness (the adjusted odds ratios [OR] ranged from 2.08 to 3.77 in Alberta and from 1.28 to 1.46 in BC). Residents in long-term care in Alberta and BC had less than half the odds of being transferred to hospital, independent of all other factors, when compared with long-term care residents in Ontario (the adjusted OR ranged from 0.38 to 0.39 in Alberta and from 0.33 to 0.44 in BC). INTERPRETATION: Significant variations in transfer rates were observed between provinces, even after controlling for individual patient characteristics. These results suggest that transfers to hospital are largely driven by health care policies, health care professional practice patterns and available infrastructure rather than individual patient needs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".