A Comparison of Mental Health, Food Insecurity, and Diet Quality Indicators between Foreign-Born Immigrants of Canada and Native-Born Canadians
Bibliographic record
Abstract
Because successful integration post-immigration is critical to Canada’s population health, national data were analyzed to examine mental health, food insecurity (Household Food Security Survey Module), and diet quality (e.g., nutrient intakes) between foreign-born immigrants and native-born Canadians. After controlling for sociodemographic and health covariates, immigrants were more likely to report poor mental health, food insecurity, and poor diet quality (ORs 1.038–1.080); consume less carbohydrates, vitamin B1, and iron (ORs 0.814–0.996); and have higher intakes of fat, fiber, and vitamins B6 and B9 (ORs 1.003–1.420; all p’s<0.05). These results can help to advance policy developments to help mitigate the ‘healthy immigrant effect’.
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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".