Acute care hospitalization of refugees to Canada: Linked data for immigrants from Poland, Vietnam and the Middle East.
Bibliographic record
Abstract
BACKGROUND: Refugees arrive in Canada with settlement challenges different from those faced by other immigrants, including a higher risk of poor health. This study reports hospitalization rates for the three fiscal years from 2006/2007 through 2008/2009 for immigrants who arrived during the 1980-to-2006 period, with a focus on three refugee groups. DATA AND METHODS: Information from two linked databases was used to estimate age-standardized hospitalization rates (ASHRs) per 10,000 population aged 30 or older for all causes (excluding pregnancy) and for leading causes, by immigrant category and by refugee subcategory. The analysis focused on refugees from Poland, Vietnam and the Middle East, whose hospitalization rates were compared with those of the Canadian-born population and/or economic class immigrants from the same areas. RESULTS: Immigrants aged 30 or older, including refugees, had significantly lower all-cause ASHRs than did the Canadian-born population. All-cause ASHRs were 470 per 10,000 for immigrants overall and 494 for refugees, compared with 891 for the Canadian-born. Of the three source areas, immigrants and refugees from Vietnam had lower ASHRs. The circulatory disease-specific ASHR for government-assisted refugees from the Middle East was similar to that of the Canadian-born population (142 and 158, respectively). Except for those from Poland, refugees typically had higher ASHRs than did their economic class counterparts. INTERPRETATION: Refugees, like other immigrants, generally had lower hospitalization rates than did the Canadian-born population, but some subgroups were particularly susceptible to hospitalization for specific chronic diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".