Bibliographic record
Abstract
BACKGROUND: A small fraction of the population accounts for a disproportionate share of health care spending and resources. Linking data from health surveys with hospital and death records offers an opportunity to examine high use of acute care in more depth than is possible with administrative data alone. DATA AND METHODS: Data for 62,675 respondents to three cycles of the Canadian Community Health Survey were linked to the Discharge Abstract Database and the Canadian Mortality Database. Respondents were categorized according to cumulative annual days in hospital: high users (30 days or more), non-high users (1 to 29), or not hospitalized. Cross-tabulations stratified by age (50 to 74 and 75 or older) were used to describe the socio-demographic, health, health behaviour, and hospital experience characteristics of the three groups. Multinomial logit and logistic regression were used to examine associations between these characteristics and high use or no hospitalization versus non-high use. RESULTS: High users made up 0.5% of the population aged 50 to 74 and 2.6% of those aged 75 or older, but they accounted for 45.6% and 56.1%, respectively, of the days that people of these ages spent in hospital. Not having a partner, being at the end of life, having a neurological condition, as well as inactivity and comorbidity (ages 50 to 74) increased the odds of high use. Being female, not having major chronic conditions, not being at the end of life, normal/overweight, and being active were associated with no hospitalization. INTERPRETATION: Linking survey, hospital, and death data improves understanding of factors associated with high hospital use.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".