Migration and health in Canada: health in the global village
Bibliographic record
Abstract
BACKGROUND: Immigration has been and remains an important force shaping Canadian demography and identity. Health characteristics associated with the movement of large numbers of people have current and future implications for migrants, health practitioners and health systems. We aimed to identify demographics and health status data for migrant populations in Canada. METHODS: We systematically searched Ovid MEDLINE (1996-2009) and other relevant web-based databases to examine immigrant selection processes, demographic statistics, health status from population studies and health service implications associated with migration to Canada. Studies and data were selected based on relevance, use of recent data and quality. RESULTS: Currently, immigration represents two-thirds of Canada's population growth, and immigrants make up more than 20% of the nation's population. Both of these metrics are expected to increase. In general, newly arriving immigrants are healthier than the Canadian population, but over time there is a decline in this healthy immigrant effect. Immigrants and children born to new immigrants represent growing cohorts; in some metropolitan regions of Canada, they represent the majority of the patient population. Access to health services and health conditions of some migrant populations differ from patterns among Canadian-born patients, and these disparities have implications for preventive care and provision of health services. INTERPRETATION: Because the health characteristics of some migrant populations vary according to their origin and experience, improved understanding of the scope and nature of the immigration process will help practitioners who will be increasingly involved in the care of immigrant populations, including prevention, early detection of disease and treatment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.022 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".