Sociodemographic Determinants of Antenatal Care Utilization in Indonesia: A Multilevel Mixed-effect Analysis
Bibliographic record
Abstract
Introduction: Antenatal Care (ANC) services provide essential interventions that effectively educate pregnant women regarding safe delivery, healthy pregnancy and postpartum family planning. This study assessed the individual and community factors associated with ANC utilisation among women aged 15–49 in Indonesia. Materials and methods: Data from the Indonesia Demographic and Health Survey 2017 were used with a sample size of 15,021 women aged 15–49 who gave birth five years before the survey. Multilevel mixed-effect logistic regression analysis was fitted to analyse the predictors of ANC utilisation after potential confounders made adjustments. A 95% confidence interval with adjusted odds ratio was reported. The community factor is the place of residence. Results: About 90% of women visited ANC services at least four times. Birth order one or two (AOR=1.69, 95% CI:1.47–1.94), women aged 20–34 (AOR=2.41, 95% CI:1.82–3.20), and women aged 35 or older (AOR=2.68, 95% CI:1.97-3.65) were factors positively associated with ANC utilisation. Higher household status, including middle-class wealth women (AOR=1.62, 95% CI:1.38–1.90) and wealthy women (AOR=2.51, 95% CI:2.10–2.99) were four times more likely to use ANC services compared to their counterparts. Secondary (AOR=1.77, 95% CI:1.56–2.02) and higher (AOR=2.70, 95% CI:2.09-3.48) educated women were associated with increased odds of ANC utilisation than primary or less educated women. Conclusion: Maternal age, birth order, maternal education, and household wealth status significantly impact ANC service utilisation. Exploring women’s and healthcare providers' views on ANC services, especially the quality of ANC services, may be beneficial for further studies.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".