Chronic health disparities among refugee and immigrant children in Canada
Bibliographic record
Abstract
There are knowledge gaps in our understanding of the development of chronic disease risks in children, especially with regard to the risk differentials experienced by immigrants and refugees. The Healthy Immigrant Children study employed a mixed-methods cross-sectional study design to characterize the health and nutritional status of 300 immigrant and refugee children aged 3-13 years who had been in Canada for less than 5 years. Quantitative data regarding socioeconomic status, food security, physical activity, diet, and bone and body composition and anthropometric measurements were collected. Qualitative data regarding their experiences with accessing health care and their family lifestyle habits were gathered through in-depth interviews with the parents of newcomer children. Many newcomers spoke about their struggles to attain their desired standard of living. Regarding health outcomes, significantly more refugees (23%) had stunted growth when compared with immigrants (5%). Older children, those with better-educated parents, and those who consumed a poorer-quality diet were at a higher risk of being overweight or obese. Sixty percent of refugees and 42% of immigrants had high blood cholesterol. Significant health concerns for refugee children include stunting and high blood cholesterol levels, and emerging trends indicate that older immigrant children from privileged backgrounds in low-income countries may be more at risk of overweight and obesity. A variety of pathways related to their families' conceptualization of life in Canada and the social structures that limit progress to meeting their goals likely influence the development of health inequity among refugee and immigrant children. Public health initiatives should address these health inequities among newcomer families.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".