Iron intake, supplement adherence, and perceived social support as predictors of anemia in rural Indonesia: A cross-sectional study
Bibliographic record
Abstract
Anemia among pregnant women remains a significant public health issue in Indonesia, particularly in Purwasari District, where 37.1% of women are anemic. Anemia may be directly influenced by social and supplementation factors, which also affect nutritional status, fetal health, and pregnancy outcomes through environmental support and the fulfillment of essential nutrient needs. This study aimed to analyze the association between social, supplementation, and nutritional factors and anemia among pregnant women in Purwasari District. A quantitative approach with a cross-sectional design was used. The sample consisted of 150 pregnant women who met the inclusion criteria of this study. Eligible participants were pregnant women aged 18–40 years in their second or third trimester. Data were analyzed using logistic regression analysis. The results showed that adequate vitamin C intake (p = 0.004; OR = 2.912), adherence to iron–folic acid (IFA) tablet consumption (p = 0.000; OR = 4.030), side effects of IFA intake (p = 0.003; OR = 3.027), and support from parents or in-laws (p = 0.026; OR = 2.563) were significantly associated with anemia in pregnancy. In conclusion, vitamin C adequacy, IFA adherence and side effects, and family support were significantly related to the occurrence of anemia among pregnant women in the Purwasari District.
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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 | 0.000 | 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) | 0.001 | 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".