Intake of Foods That Could Be Fortified and of Nutrients That Could Potentially Contribute to Anemia Among Indian Women Before Fortification Implementation
Bibliographic record
Abstract
1) To estimate intake of staple foods and condiments that could be fortified if 2018 fortification regulations released by the Food Safety and Standards Authority of India for oil, wheat flour, rice, salt, and milk were implemented effectively under social protection programs; and 2) To estimate intake of nutrients that could potentially contribute to anemia among women of reproductive age (WRA) (15–49 y) in India, prior to fortification implementation. We estimated WRA's mean food intake and intake of iron, vitamin A, vitamin C, riboflavin, thiamine, zinc, and folate by integrating single-day 24-h dietary recall from the National Nutrition Monitoring Bureau (NNMB) Rural Survey 2009–2012 (n = 11,625) and our food composition table (FCT). This FCT was created using the 1989 and 2017 Indian FCTs, FCT for Bangladesh, and USDA's Food Data Central to estimate WRA's intake of nutrients that were not included in the original NNMB: copper, vitamin B12, vitamin B6, and vitamin E. On a daily basis prior to fortification, WRA consumed on average 10.5 (SD 11.6) g of oil, 78.9 (SD 133.8) g of wheat flour, 227.4 (SD 158.8) g of rice, 0.4 (SD 4.6) g of salt, and 55.4 (SD 79.7) g of milk. On a daily basis, 73.1%, 45.5%, 85.5%, 12.8%, and 59.5% of WRA consumed oil, wheat flour, rice, salt and milk, respectively. Prior to fortification, WRA consumed on average 12.3 (SD 7.4) mg of iron, 190.7 (SD 473.1) mcg of vitamin A, 37.8 (SD 36.8) mg of vitamin C, 0.7 (SD 0.3), mg of riboflavin, 1.1 (SD 0.5) mg of thiamine, 7.8 (SD 3.6) mg of zinc, 235.4 (SD 419.6) mcg of folate, 1.9 (SD 0.8) mg of copper, 1.3 (SD 3.3) mcg of vitamin B12, 1.1 (SD 0.5) mg of vitamin B6, and 3.3 (SD 3.5) mg of vitamin E. Staple food consumption suggests that wheat flour, rice, and milk are good fortification vehicles to reach WRA. The percentage of women consuming condiments suggests oil is a good fortification vehicle for WRA. However, WRA's intake of nutrients that could potentially contribute to anemia is varied. Our food composition table provides a unique opportunity to analyze nutrients in addition to those included in the NNMB. The NNMB data can be used to model the potential nutrient contribution of fortified foods among WRA in India. Global Affairs Canada.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".