A Comparison of the Predictors of Heart Health Among Immigrants and Native-Born Canadians
Bibliographic record
Abstract
With over 18% of the Canadian population born outside of Canada, the health of immigrants is an important concern. Heart health is of particular importance because heart disease is the leading cause of death among men and women in Canada. Using data from the National Population Health Survey (NPHS), the purpose of this thesis is to first establish whether immigrants to Canada have lower rates of coronary heart disease (CHD), and high blood pressure (HBP) than native-born Canadians, and second to determine the lifestyle and psychosocial factors that predict heart health and compare them between immigrants and native-born Canadians. Regression and survival analyses of the NPHS data indicate that lifestyle and psychosocial risk factors such as smoking status, body mass index, alcohol consumption and depression affect immigrants and native-born Canadians similarly. Immigration variables such as length of time in the host country and country of origin are significant risk factors for HBP, however, not in the incidence of CHD. Immigrants were more likely to have HBP than native-born Canadians. However, immigrants have a significantly lower incidence of CHD than native-born Canadians. Native-born Canadians are at a higher risk of heart disease at a younger age than immigrants. These results suggest that there must be other factors relating to immigration affecting the heart health of immigrants. Due to the complexity and high incidence of heart disease in Canada, it may never be possible to ascertain all of the risk factors for heart disease. However, this study has identified several key risk factors and has excluded other variables as possible risk factors. The risk factors identified in this study can form the basis for the development of heart health programs to target all Canadians-both native-and foreign-born.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".