Does improving the iron status with double fortified salt affect nutrient intakes of women tea plantation workers in West Bengal, India?
Bibliographic record
Abstract
Iron deficiency is highly prevalent in India and affects women's health status. A randomized double‐masked study tested the efficacy of salt dually fortified with iron and iodine (DFS) to improve the iron status of female tea plantation employees in West Bengal, India. Study participants (N=217) were randomized to iodized salt or DFS and followed for 10 mo. The women were at least 18 y, not pregnant or lactating, and experienced full‐time tea pickers. Diets were assessed at 3 time points by 3 methods: 1) one weighed food intake during lunch, 2) one 24‐hr recall, and 3) one 7‐d food frequency questionnaire. At baseline and endline, women shared their lunch with co‐workers in the tea garden; lunch at midpoint was consumed at home. The analysis examined food types, ingredients, and amounts (g) served, consumed, and shared, and required the development of a nutrient composition database for 334 food items consumed. We hypothesized that improving the iron status of female workers would increase energy and nutrient intakes through different mechanisms, including an increase in appetite and an improvement in food accessibility through better work capacity and higher incomes. This study is the first to examine the nutrient intakes of women tea plantation workers consuming DFS. Supported by the Mathile Institute for the Advancement of Human Nutrition and the Micronutrient Initiative
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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 | 0.000 |
| 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) | 0.001 | 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".