Variation in Emergency Department Transfer Rates from Nursing Homes in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Nursing home (NH) residents are frequently transferred to the emergency department (ED) but there is little data on inter-facility variation, which has implications for intervention planning and implementation. OBJECTIVES: To describe variation in ED transfer rates (TRs) across NHs and the association with NH characteristics. DESIGN/SETTING: Retrospective cohort study using linked administrative data from Ontario. PARTICIPANTS: 71,780 residents of 604 NHs in 2010 and followed for one year. MEASUREMENTS: Funnel plots were used to identify high transfer NHs and logistic regression to test the association with NH location, size, ownership and historical ED transfer rate. RESULTS: One-year ED transfer rates ranged from 4.3% to 58.6% (mean 28.4%); 115 (19%) NHs were considered high. Being within five minutes of an ED, larger size and high historical ED transfer rate were associated with being a high ED transfer home. CONCLUSION: There was substantial variation across NHs. Consideration of characteristics such as proximity to an ED may be important in the development and targeting of different interventions for NHs.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".