“Midwives do not appreciate pregnant women who come to the maternity with torn and dirty clothing”: institutional delivery and postnatal care in Torit County, South Sudan: a mixed method study
Bibliographic record
Abstract
BACKGROUND: South Sudan has one of the highest maternal mortality ratios in the world, at 789 deaths per 100,000 live births. The majority of these deaths are due to complications during labor and delivery. Institutional delivery under the care of skilled attendants is a proven, effective intervention to avert some deaths. The aim was to determine the prevalence and explore the factors that affect utilization of health facilities for routine delivery and postnatal care in Torit County, South Sudan. METHODS: A convergent parallel mixed method design combined a community survey among women who had delivered in the previous 12 months selected through a multistage sampling technique (n = 418) with an exploratory descriptive qualitative study. Interviews (n = 19) were conducted with policymakers, staff from non-governmental organizations and health workers. Focus group discussions (n = 12) were conducted among men and women within the communities. Bivariate and multivariate logistic regression were conducted to determine independent factors associated with institutional delivery. Thematic analysis was undertaken for the qualitative data. RESULTS: Of 418 participants who had delivered in the previous 12 months, 27.7% had institutional deliveries and 22.5% attended postnatal care at least once within 42 days following delivery. Four or more antenatal care visits increased institutional delivery 5 times (p < 0.001). The participants who had an institutional delivery were younger (mean age 23.3 years old) than those who had home deliveries (mean age 25.6 years). Any previous payments made for delivery in the health facility doubled the risk of home delivery (p = 0.021). Women were more likely to plan and prepare for home delivery than for institutional delivery and sought institutional delivery when complications arose. Perceived poor quality of care due to absence of health staff and lack of supplies was reported as a major barrier to institutional delivery. Women emphasized fear of discrimination based on social and economic status. Unofficial payments such as soap and sweets were reported as routine expectations and another major barrier to institutional delivery. CONCLUSION: Interventions to stop unofficial payments and discrimination based on socio-economic status and to increase access to ANC, delivery services and PNC are needed.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".