Hospitalization and Mortality Rates in Long-Term Care Facilities: Does For-Profit Status Matter?
Bibliographic record
Abstract
OBJECTIVES: To establish if proprietary status (ie, for-profit or not-for-profit) is associated with mortality and hospitalizations among publicly funded long-term care (nursing) homes. METHODS: We conducted a retrospective cohort study of new admissions in 640 publicly funded long-term care facilities in Ontario, Canada (384 for-profit, 256 not-for-profit). A population-based cohort of 53,739 incident admissions into long-term care facilities between January 1, 2010, and March 1, 2012, was observed. We measured adjusted rates of hospital admissions and mortality, per 1000 person-years (PY) of follow-up, among for-profit and not-for-profit facilities at 3, 6, and 12 months postadmission. Rates were measured postadmission and until discharge or death, whichever came first. RESULTS: One year after admission and before discharge, 11.7% of residents died and 25.7% had at least one hospitalization. After 12 months of follow-up, residents in for-profit facilities had a hospitalization rate of 462 per 1000 PY versus 358 per 1000 PY in not-for-profit facilities. During this period, the crude mortality rate in for-profit facilities was 208 per 1000 PY versus 185 per 1000 PY in not-for-profit facilities. At 3, 6, and 1 year after admission, for-profit facilities had an adjusted hazard ratio of 1.36 (95% confidence interval [CI] 1.28-1.43), 1.33 (95% CI 1.27-1.39), and 1.25 (95% CI 1.21-1.30) for hospitalizations and hazards of 1.20 (95% CI 1.11-1.29), 1.16 (95% CI 1.09-1.24), and 1.10 (95% CI 1.05-1.16) for mortality, respectively. CONCLUSIONS: Publicly funded for-profit facilities have significantly higher rates of both mortality and hospital admissions.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".