Who are the High Hospital Users? A Canadian Case Study
Bibliographic record
Abstract
OBJECTIVES: Researchers have taken two different approaches to understanding high use of hospital services, one focusing on the large proportion of services used by a small minority and a second focusing on the poor health status and high hospital use of the poor. This work attempts to bridge these two widely researched approaches to understanding health care use. METHODS: Administrative data from Winnipeg, Manitoba covering all hospitalizations in 1995 were combined with public use Census measures of socio-economic status (neighbourhood household income). High users were defined as the 1% of the population who spent the most days in hospital in 1995 (n = 6487 hospital users out of population of 648715 including non-users). RESULTS: One per cent of the Winnipeg population consumed 69% of the hospital days in 1995. Thirty-one per cent of the highest users were among the 20% of residents of neighbourhoods with the lowest household incomes, and 10% of the highest users were among the 20% from neighbourhoods with the highest household incomes. However, on most other dimensions, including gender, age, average days in hospital, average admissions, percentage who died in hospital and diagnostic reasons for being hospitalized, the similarities between high users, regardless of their socio-economic group, were striking. CONCLUSIONS: The lower the socio-economic status, the more likely an individual is to make high demands on hospitals. However, patterns of use as well as the diseases and accidents that produce high use among residents of low income neighbourhoods are not much different from those that produce high use among residents of high income neighbourhoods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".