Rankings of iron, vitamin D, and calcium intakes in relation to maternal characteristics of pregnant Canadian women
Bibliographic record
Abstract
Iron, vitamin D, and calcium intakes in the prenatal period are important determinants of maternal and fetal health. The objective of this study was to examine iron, vitamin D, and calcium intakes from diet and supplements in relation to maternal characteristics. Data were collected in a subsample of 1186 pregnant women from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a cohort study including pregnant women recruited from 10 Canadian sites between 2008 and 2011. A food frequency questionnaire was administered to obtain rankings of iron, calcium, and vitamin D intake (16-21 weeks of pregnancy). Intakes from supplements were obtained from a separate questionnaire (6-13 weeks of pregnancy). Women were divided into 2 groups according to the median total intake of each nutrient. Supplement intake was an important contributor to total iron intake (median 74%, interquartile range (IQR) 0%-81%) and total vitamin D intake (median 60%, IQR 0%-73%), while the opposite was observed for calcium (median 18%, IQR 0%-27%). Being born outside of Canada was significantly associated with lower total intakes of iron, vitamin D, and calcium (p ≤ 0.01 for all). Consistent positive indicators of supplement use (iron, vitamin D, and calcium) were maternal age over 30 years and holding a university degree. In conclusion, among Canadian women, the probability of having lower iron, vitamin D, and calcium intakes is higher among those born outside Canada; supplement intake is a major contributor to total iron and vitamin D intakes; and higher education level and age over 30 years are associated with supplement intake.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".