Who Are High Users of Hospitals in Canada? Findings From a Population-Based Study
Bibliographic record
Abstract
Background Dying people and older people have often been thought of as high users of hospitals, but current population-based evidence is needed to confirm or refute this claim. Purpose Quantitative population-based study designed to identify and describe hospital patients who are high users. Methods Data for all 2014–2015 Canadian hospital patients (excluding Quebec) were analyzed to identify and describe high users through descriptive-comparative and regression analysis tests. Results Only a small proportion of patients are high users in relation to multiple admissions or 30+ inpatient days of care, and with considerable diversity among them and relatively few of these advanced in age or dying in hospital. Conclusions Relatively few patients are high users of hospitals. These people are most often under age 65, so they have the potential to be ill and high users for many years. Flagging would enable individualized care planning to reduce illness exacerbations or slow disease progression and address other risk factors for long or repeat hospitalizations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".