Does Combining Antenatal Care Visits at Health Posts and Health Centers Improve Antenatal Care Quality in Rural Ethiopia?
Bibliographic record
Abstract
Background: The COVID-19 pandemic is putting a pressure on global health systems. The disruption of essential health services (EHS) has an impact on the health of mothers, neonate and children in developing countries. Therefore, the main aim of this study was assessing the availability of Maternal, Newborn care and Child health (MNCHS) services at primary health care unit during COVID-19 outbreak. Methods: A cross-sectional survey was conducted in five regions of Ethiopia in 2021. Descriptive analyses were undertaken using STATA 16 software and the results presented using tables and different graphs. A continuity of EHS assessment tool adopted from WHO was used for data collection. Result: During COVID -19 pandemic, 30 (69.8%) of woreda health offices, 52 (56.5%) of health centers (HCs), 7 (44.4%) of hospitals, and 165 (48%) of health posts (HPs) had a defined list of EHS. In comparison with other EHS, family planning is the least available service in all regions. At HPs level care for sick children and antenatal care (ANC) were available at 59.1 and 58.82% respectively. Except immunization services at SNNP, all other maternal, newborn, and child health EHS were not available to all HPs at full scale. Conclusion: Immunization services were most available, while ANC and care for sick children were least available during COVID-19 at the HPs level. There was regional variation in MNCH EHS service availability at all levels.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".