Barriers to Antenatal Care Attendance in Developing Countries: A Systematic Review
Bibliographic record
Abstract
Attendance of antenatal care (ANC) is very crucial in enhancing the health of mothers and fetuses, and many barriers deny women in developing nations the opportunity to receive this important service. This systematic review aimed to identify the major obstacles and facilitators of ANC attendance and explore the determinants of healthcare use in pregnant women. A comprehensive search of PubMed, Google Scholar, and Web of Science was conducted for studies published between 2015 and 2025. Ten studies met the inclusion criteria. These cross-country studies (in Ethiopia, Nigeria, Tanzania, Bangladesh, and Malawi) revealed information on the socioeconomic, cultural, and healthcare system barriers that women are challenged by. The review identified financial constraints, transportation issues, and cultural beliefs as the primary barriers to attending antenatal care. Facilitators associated with this were community-based health programs, male participation, and enhanced healthcare facilities. Specifically, Tanzanian and Ethiopian studies have highlighted the role of health education and community health workers (CHWs) in facilitating ANC attendance. The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) for non-randomized studies, and the majority of studies demonstrated a low to moderate risk. The results indicate that overcoming economic and cultural barriers, along with improving healthcare access, is crucial for increasing ANC attendance. The interventions that can improve maternal health outcomes include empowerment initiatives, community-based programs, and strategies to enhance transportation access. Future studies should be guided by longitudinal designs and randomized experiments to determine the long-term effects of these interventions on ANC attendance and maternal health.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".