Immigration factors and potentially avoidable hospitalizations in Canada
Bibliographic record
Abstract
OBJECTIVE: Estimate the effect of immigration characteristics on the risk of a hospitalization for an ambulatory care sensitive condition (ACSC). RESEARCH DESIGN: We analyzed data on the Canadian resident adult population aged 18 to 74 years who responded to the 2006 long form Census. The Census data were linked to the Canadian Institute for Health Information (CIHI)'s Discharge Abstract Database (DAD) for fiscal years 2006-2007, 2007-2008, and 2008-2009. We conducted a logistic regression on the binary variable we created for an ACSC admission. MEASURES: The CIHI definition of ACSC hospitalizations was used to identify potentially avoidable hospitalizations in the DAD. Immigration factors analyzed included years in Canada, ethnic origin, and ability to speak one of the official languages. RESULTS: There were 3,342,450 respondents aged between 18 and 74. Using the Canadian at birth as our reference population, recent immigrants (up to five years in Canada) had lower odds of an ACSC hospitalization, regardless of their ethnic origins, with the exception of immigrants from Oceania and from other North American countries for whom the effect was not significant. The protective effect was still present in children of immigrants (AOR=0.89). Immigrants from the Caribbean, from Southern, Eastern, and Western Europe, as well as those from East Asia had lower odds across categories of time spent in Canada. The protective effect was stronger in immigrants from East Asia and lower in those of Oceanic and other North American countries. CONCLUSIONS: Our results suggest that the healthy immigrant effect dissipates with time in Canada but remains even in children of immigrants. The protective effect differs depending on the ethnic origin of the immigrant.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".