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Record W4417306276 · doi:10.30867/action.v10i4.2617

Iron intake, supplement adherence, and perceived social support as predictors of anemia in rural Indonesia: A cross-sectional study

2025· article· id· W4417306276 on OpenAlexaff
Yayuk Sri Rahayu, Marliana Rahma, Nita Farida

Bibliographic record

VenueAcTion Aceh Nutrition Journal · 2025
Typearticle
Languageid
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsHorizon Health Network
Fundersnot available
KeywordsAnemiaLogistic regressionPregnancySocial supportIron supplementAffect (linguistics)Family supportIron supplementation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.020
GPT teacher head0.333
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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