Hospitalization rates among economic immigrants to Canada.
Bibliographic record
Abstract
BACKGROUND: Economic immigrants generally, and economic class principal applicants (ECPAs) specifically, tend to have better health than other immigrants. However, health outcomes vary among subcategories within this group, especially by sex. DATA AND METHODS: This study examines hospitalization rates among ECPAs aged 25 to 74 who arrived in Canada between 1980 and 2006 as skilled workers, business immigrants, or live-in caregivers. The analysis used two linked databases to estimate age-standardized hospitalization rates (ASHRs) overall and for leading causes by sex. ASHRs of ECPA subcategories were compared with each other and with those of the Canadian-born population. Logistic regression was used to derive odds ratios for hospitalization among ECPAs, by sex. RESULTS: Male and female ECPAs aged 25 to 74 had significantly lower all-cause ASHRs than did the Canadian-born population in the same age range. This pattern prevailed for each ECPA subcategory and for each disease examined. Compared with skilled workers, business immigrants had lower odds of hospitalization; live-in caregivers who arrived after 1992 had higher odds. Adjustment for education, official language proficiency, and world region reduced the strength of or eliminated these associations. INTERPRETATION: Compared with the Canadian-born population, ECPAs generally had low hospitalization rates. Differences were apparent among ECPA subcategories.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".