Canadian newcomer children’s bone health and vitamin D status
Bibliographic record
Abstract
Adequate calcium intake and supply of vitamin D during childhood play important roles in ensuring adequate bone mass gain to achieve optimal peak bone mass. The Healthy Immigrant Children study employed a mixed-method 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. This paper presents bone mineral content and vitamin D status data along with qualitative data that deepen the understanding of newcomer bone health status. A significantly higher percentage of refugee children (72.3%) had insufficient (<50 nmol/L) or deficient (<30 nmol/L) serum vitamin D compared with immigrants (53.2%). Vitamin D deficiency was most common among ethnic minority girls. Newcomer children with higher intakes of vitamin D, younger newcomer children, and those from western Europe or the United States had higher serum vitamin D levels. Immigrants had significantly higher mean total body bone mineral content compared with refugees. Total body fat, serum vitamin D, calcium intake, height, height by calcium intake, total body fat by calcium intake, and total body fat by height predicted total body bone mineral content levels. Vitamin D deficiency among newcomer children may be related to lack of knowledge regarding children's vitamin D requirements in the Canadian environment, dietary habits established in country of origin, low income that limits healthy dietary choices, and lifestyle habits that limit exposure to sunlight. Results suggest a need to screen newcomer children and pregnant women for vitamin D deficiency and support early intervention.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".