Education and improved iron intakes for treatment of mild iron deficiency anemia in adolescent girls in southern Benin
Bibliographic record
Abstract
The impact of a nutrition education program combined with an increase in bioavailable dietary iron to treat iron deficiency anemia (IDA) has never been studied in adolescent girls. The impact of an intensive dietary program for the treatment of IDA was studied in 34 intervention and 34 control boarding girls aged 12 to 17 years from Benin. A quasi‐experimental design comprising 4 weeks of nutrition education combined with an increase in the content and bioavailability of dietary iron for 22 weeks was implemented in the intervention school, but not in the control school. A nutrition knowledge questionnaire, 24‐h dietary recalls, anthropometric measurements, iron status indices and screening for malarial and intestinal parasitic infections (IPI) were obtained in both groups. Nutrition knowledge scores and mean intakes of nutrients including dietary iron, absorbable iron and vitamin C were higher in the intervention group (p < 0.05) compared to the control group after 26 weeks. Also, mean hemoglobin and serum ferritin values were higher (122 vs. 113g/L; p = 0.0002; 32 vs. 19µg/L; p = 0.04) in the intervention group, whereas the incidence of anemia (32 vs 85%; p = 0.005) and IDA (26 vs. 56%; p = 0.04) was significantly lower. This study is the first, to our knowledge, to demonstrate in adolescent girls that a multi‐dietary strategy aiming to improve available dietary iron can reduce iron deficiency anemia.
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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 | 0.001 | 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".