Vitamin and Micronutrient Intakes among Women of Reproductive Age in Vietnam
Bibliographic record
Abstract
Micronutrient deficiencies are a public health concern in Vietnam negatively affecting maternal and child health outcomes. However, information on micronutrient intake among women of reproductive age (WRA) is lacking. Data from a survey of 5011 WRA participating in preconceptional micronutrient supplementation trial (PRECONCEPT) in Thai Nguyen province was used to identify food sources and estimate the adequacy and determinants of micronutrient intakes. Dietary intake was assessed using a validated 107‐item food‐frequency questionnaire. Starchy staples were the main source of iron and zinc (33% and 50%, respectively) with only a small proportion from meat (9% and 17%, respectively). The primary source of folate and vitamin A were vegetables; vitamin B12 came from meat and eggs. Median intake of most micronutrients were within recommended daily intake, but inadequate intakes were identified for iron (41%), zinc (7.0%), folate (54%), vitamins B12 (62%) and A (32%). Socioeconomic status was the main determinant of micronutrient intakes. WRA in the wealthiest quintile consumed 26% more iron, 19% more zinc, 36% more folate, 82% more vitamin B12 and 47% more vitamin A compared to those in the poorest quintile. Targeted efforts to promote the consumption of local nutrient rich foods along with educational programs and social development are needed. Funding: The Mathile Inst. and the MI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".