Chronic disease and malnutrition biomarkers among unemployed immigrants and Canadian born adults
Bibliographic record
Abstract
BACKGROUND: Immigration status and unemployment may intersect on the health outcomes of men and women. This study aimed to identify intersections between unemployment and immigration in inflammatory, metabolic and nutritional blood markers and assess gender differences. METHODS: We used Canadian Health Measures Survey data on 2493 participants aged 18 to 65. Outcomes were chronic inflammation (high-sensitivity C-reactive protein (hsCRP) and fibrinogen), nutritional (albumin and hemoglobin), and metabolic blood markers (glycosylated hemoglobin, blood glucose, total and high density lipoprotein (HDL) cholesterol). Multivariate linear regressions were used to assess the associations between each biomarker, unemployment and immigrant status, controlling for age, education, province, smoking, physical inactivity and body mass index and testing for multiplicative interactions between unemployment, immigrant status and gender. RESULTS: Unemployment was associated with higher inflammation (hsCRP and fibrinogen) in Canadian born men; Canadian born employed women showed higher hsCRP values compared with corresponding employed men. Unemployed immigrant women presented the highest values of hsCRP while employed immigrant women had the lowest hsCRP. Unemployment was associated with higher glucose; immigrant status was associated with higher glucose and glycosylated hemoglobin. Unemployed immigrants had significantly lower levels of hemoglobin and albumin than employed immigrants, and Canadian-born citizens regardless of their employment status. Some of these associations were attenuated after adjustment by body mass index, physical inactivity and smoking. CONCLUSION: Blood biomarkers unveil intersections among unemployment, immigration and gender. This study provides evidence on biological pathways of unemployment on the likelihood of common chronic diseases, inflammation and potential malnutrition with some increased vulnerabilities in unemployed immigrants, and particularly in unemployed immigrant women.
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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".