Health Facility Capacity to Provide Maternal and Newborn Healthcare Services in Unguja
Bibliographic record
Abstract
Globally, every year 529,000 maternal deaths occur, 99% of which in developing countries with majority being in Sub-Saharan Africa. Maternal, Newborn and Child Health (MNCH) services depend on the accessibility, availability and quality of antenatal care (ANC), delivery and postnatal services. The aim of this study was to assess the health facilities’ capacity and readiness to provide MNCH services in Unguja Island, Zanzibar. A facility-based cross-sectional survey was conducted from May to June 2015 at public health facilities providing MNCH services. Data was collected by using the modified Service Availability and Readiness Assessment tool. Eighteen health facilities were assessed, two-thirds (66.7%, n = 12) of which were offering both maternity and reproductive and child health (RCH) services, 4 (22.2%) RCH services only, and 2 (11.1%) maternity services only. Readiness score for ANC services was 66% with high readiness scores in diagnostics services (89%) and equipment (69%). Overall, 14% offered all seven signal functions. Overall, delivery service readiness score was 48%. Overall readiness for comprehensive emergency obstetric and neonatal care services was 13%. Staff training and guidelines readiness score was 11%, while medicine and commodities score was 9%. The health facilities’ readiness in providing MNCH services remains inadequate in Unguja Island. Readiness in providing services was low for delivery and emergency obstetric and neonatal care services. Basic and advanced delivery services need to be improved in parallel with provision of necessary equipment, medicines and commodities and staff training for better MNCH service delivery.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".