A mixed methods assessment of barriers to maternal, newborn and child health in Gogrial West, South Sudan
Bibliographic record
Abstract
BACKGROUND: Health conditions for mothers, newborns, and children in South Sudan are among the worst worldwide. South Sudan has the highest rate of maternal mortality in the world and despite alarming statistics, few women and children in South Sudan have access to needed healthcare, especially in rural areas. The purpose of this study was to understand the barriers to maternal, newborn and child health in Gogrial West, Warrap State, South Sudan, one of the most underdeveloped states. METHODS: A randomized household quantitative study and supplemental qualitative interviews were employed in 8/9 payams in Gogrial West, Warrap, South Sudan. Interviews were conducted with randomly selected female household members (n = 860) who were pregnant or had children less than 5 years of age, and men (n = 144) with a wife having these characteristics. Non-randomized qualitative interviews (n = 72) were used to nuance and add important socio-cultural context to the quantitative data. Analysis involved the estimation of weighted population means and percentages, using 95% confidence intervals and considering p-values as significant when less than 0.05, when comparisons by age, age of marriage, wife status and wealth were to be established. RESULTS: Most women (90.8%) and men (96.6%) did not want contraception. Only 1.2% of women aged 15-49 had met their need for family planning. On average, pregnant women presented for antenatal care (ANC) 2.3 times and by unskilled providers. Less than half of households had a mosquito net; fewer had insecticide treated nets. Recognition of maternal, newborn and child health danger signs overall was low. Only 4.6% of women had skilled birth attendants. One quarter of children had verifiable DPT3 immunization. Five percent of men and 6% of women reported forced intercourse. Overall men and women accept beatings as a norm. CONCLUSION: Barriers to care for mothers, infants and children are far more than the lack of antenatal care. Maternal, newborn and child health suffers from lack of skilled providers, resources, distance to clinics. A lack of gender equity and accepted negative social norms impedes healthy behaviors among women and children. The paucity of a peer-reviewed evidence base in the world's newest country to address the overwhelming needs of the population suggests these data will help to align health priorities to guide programmatic strategy for key stakeholders.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".