Determinants, Prevention, and Incidence of Cardiovascular Disease Among Immigrant and Refugee Populations
Bibliographic record
Abstract
Immigration policies shape the composition, socioeconomic characteristics, and health of migrant populations. The health of migrants is also influenced by a confluence of social, economic, environmental, and political factors. Immigrants and refugees often face various barriers to accessing health care because of factors such as lack of familiarity with navigating the health care system, language barriers, systemic racism, and gaps in health insurance. Social determinants of health and access to primary care health services likely influence the burden of cardiovascular risk factors among immigrants. The relatively low burden of many cardiovascular risk factors in many immigrant populations likely contributes to the generally lower incidence rates of acute myocardial infarction, heart failure, and stroke in immigrants compared with nonimmigrants, although cardiovascular disease incidence rates vary substantially by country of origin. The "healthy immigrant effect" is the hypothesis that immigrants to high-income countries, such as Canada, are healthier than nonimmigrants in the host population. However, this effect may not apply universally across all immigrants, including recent refugees, immigrants without formal education, and unmarried immigrants. As unfolding sociopolitical events generate new waves of global migration, policymakers and health care providers need to focus on addressing social and structural determinants of health to better manage cardiovascular risk factors and prevent cardiovascular disease, especially among the most marginalized immigrants and refugees.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".