Dietary assessment of Indigenous Canadian Arctic women with a focus on pregnancy and lactation
Bibliographic record
Abstract
OBJECTIVES: To assess the diet of Indigenous women, including pregnant and lactating women, in the Canadian Arctic in terms of dietary adequacy, and to assess the contribution of traditional food to the diet. STUDY DESIGN: Population-based cross-sectional design, using 24-hour dietary recalls. METHODS; Twenty-four hour quantitative dietary recalls were collected in 47 communities in 5 surveys between 1987 and 1999, including non-pregnant and non-lactating women (n = 1300), pregnant women (n = 74) and lactating women (n = 117). Unique methods of assessment were undertaken using Software for Intake Distribution Assessment (SIDE) partitioned intra- and interindividual variance that allowed the estimation of the distribution of usual daily nutrient intakes for comparison to North American dietary reference intakes. RESULTS: Contributions of traditional Arctic food to energy intakes varied and the prevalence of inadequacies were generally high for magnesium, vitamin A, folate, vitamin C and vitamin E. Supplement use was infrequent. Many women met their needs for iron, and some exceeded the recommended upper limit for iron with food alone. Average intakes of manganese and vitamin D met recommended levels, but calcium did not. CONCLUSIONS: These results are the only data to date reporting an assessment of the dietary intakes of pregnant and lactating Canadian Arctic Indigenous women. Special attention is required for inadequacies of magnesium, zinc, calcium, folate, and vitamins E, A and C; and for use of supplements during pregnancy. Most pregnant and lactating women met iron needs without supplements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".