Quality of First Prenatal Consultations in Malemba Nkulu, Democratic Republic of Congo: Challenges and Opportunities in a Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Maternal mortality remains alarmingly high in the Democratic Republic of Congo (DRC), particularly in rural areas where access to quality prenatal care is limited. Despite global efforts to improve maternal health, systemic gaps persist in the delivery of antenatal services. OBJECTIVE: The objective of this study is to assess the quality of first antenatal consultations in the Malemba Nkulu health zone and identify structural and procedural factors contributing to substandard care. METHODS: A cross-sectional descriptive study was conducted in November 2023 across 8 health facilities selected through simple random sampling. Data were collected from 248 pregnant women attending their first prenatal visit and from 14 health care providers. Quality indicators were assessed using a structured checklist based on World Health Organization (WHO) standards. Variables included provider qualifications, availability of diagnostic tools, and completeness of clinical assessments. RESULTS: Only 2% (5/248) of first antenatal consultations met the minimum quality standards. Major deficiencies included lack of physical examinations 78% (193/248), absence of essential laboratory tests 92% (228/248), and inadequate counseling 85% (212/248). Facilities lacked basic equipment such as blood pressure monitors and hemoglobin tests. Provider training was inconsistent, and community awareness of prenatal care remained low. CONCLUSIONS: The quality of first antenatal consultations in Malemba Nkulu is critically poor, reflecting broader systemic challenges in rural maternal health care. Strengthening provider training, improving infrastructure, and enhancing community engagement are essential to reduce maternal mortality and improve outcomes in resource-limited settings.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".