Detecting and managing hypertensive disorders in pregnancy: a cross-sectional analysis of the quality of antenatal care in Nigeria
Bibliographic record
Abstract
BACKGROUND: Nigeria has one of the highest rates of maternal mortality in the world (576/100,000 births), with a significant proportion of death attributed to hypertensive disorders in pregnancy (HDPs). High quality antenatal care (ANC) plays a crucial role in early detection and management of HDPs. We conducted an assessment of quality of antenatal care, and its capacity to detect and manage HDPs, in two tiers of Nigerian facilities, with the aim of describing the state of service delivery and identifying the most urgent gaps. METHODS: Quality of antenatal care was assessed and compared between primary healthcare centers (PHCs) (n = 56) and hospitals (secondary + tertiary facilities, n = 39) in seven states of Nigeria. A cross-sectional design captured quality of care using facility inventory checklists, semi-structured interviews with healthcare providers and clients, and observations of ANC consultations. A quality of care framework and scoring system was established based on aspects of structure, process, and outcome. Average scores were compared using independent sample t-tests and measures of effect were assessed by multivariate linear regression. RESULTS: All domains of quality except provider interpersonal skills scored below 55%. The lowest overall scores were observed in provider knowledge (49.9%) and provider technical skill (47.7%). PHCs performed significantly worse than hospitals in all elements of quality except for provider interpersonal skills. Provider knowledge was significantly associated with their level of designation (i.e., obstetrician vs. other providers). CONCLUSIONS: In order to provide high quality care, ANC in Nigeria must experience massive improvements to inventory, infrastructure and provider knowledge and training. In particular, ANC programs in PHCs must be revitalized to minimize the disparity in quality of care provided between PHCs and hospitals. The relatively low quality of care observed may be contributing to Nigeria's high rate of maternal mortality and burden of disease attributed to HDPs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".