Acute care hospitalization, by immigrant category: Linking hospital data and the Immigrant Landing File in Canada.
Bibliographic record
Abstract
BACKGROUND: Although immigrants tend to be healthier than the Canadian-born population when they arrive, subgroups, notably different immigration categories, may differ in health and health care use. Data limitations have meant the research has seldom focused on category of immigrant-economic, family or refugee. A newly linked database has made it possible to study acute care hospitalization by immigration category and source region. DATA AND METHODS: The Immigrant Landing File-Hospital Discharge Abstract Linked Database (n = 2.6 million) was used to derive sex-specific crude and age-standardized hospitalization rates (ASHRs) per 10,000 population for all-cause and leading causes of hospitalization during the 2006/2007-to-2008/2009 period. RESULTS: Economic class immigrants had lower all-cause ASHRs than did their family class or refugee counterparts. Male refugees had high ASHRs overall and for circulatory diseases, digestive diseases, injury, and cancer. Female differences by immigrant class were less pronounced. All-cause ASHRs (excluding pregnancy) rose with years since arrival in Canada for male and female immigrants. Immigrants from East Asia had the lowest ASHRs; those from the United States, the highest. INTERPRETATION: Although hospital use is an imperfect indicator of health status, this study supports an initial healthy immigrant effect and its subsequent decline. Marked differences emerged among immigrant subgroups with some, notably refugees and immigrants from the United States, having significantly higher hospitalization rates overall and for leading causes, compared with other groups.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".