Factors impacting antenatal care utilization: a systematic review of 37 fragile and conflict-affected situations
Bibliographic record
Abstract
BACKGROUND: It is estimated that over 930 million people live in fragile and conflict-affected situations (FCAS) worldwide. These regions, characterized by violence, civil unrest, and war, are often governed by corrupt administrations who are unwilling to provide their citizens with basic human rights. Individuals living in FCAS face health inequities; however, women are disproportionally affected and face additional barriers to accessing sexual and reproductive services, including antenatal care (ANC). This systematic review aims to identify the factors that impact ANC usage in the 37 countries or regions classified as FCAS in 2020 by The World Bank. METHODS: Using the PRISMA guidelines, a systematic search of five databases (SCOPUS, Web of Science, PubMed, EMBASE, and CINAHL) was conducted. Results were limited to human studies, written in English, and published between January 2002 and January 2022. Studies that identified factors affecting utilization of ANC or maternal health services were included for review and critically appraised using the National Institute of Health's Quality Assessment Tools. Findings were summarized using a narrative synthesis approach. RESULTS: The database search yielded 26,527 studies. After title, abstract and full-text review, and exclusion of duplicate articles, 121 studies remained. Twenty-eight of the 37 FCAS were represented in the included studies. The studies highlighted that women in FCAS' are still not meeting the World Health Organization's 2002 recommendation of four ANC visits during pregnancy, a recommendation which has since been increased to eight visits. The most cited factors impacting ANC were socioeconomic status, education, and poor quality of ANC. Despite all studies being conducted in conflict-affected regions, only nine studies explicitly identified conflict as a direct barrier to accessing ANC. CONCLUSION: This review demonstrated that there is a paucity in the literature examining the direct and indirect impacts of conflict on ANC utilization. Specifically, research should be conducted in the nine FCAS that are not currently represented in the literature. To mitigate the barriers that prevent utilization of maternal health services identified in this review, policy makers, women utilizing ANC, and global organizations should attempt to collaborate to enact policy change at the local level.
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".