National Assessment of the Health Extension Program in Ethiopia: Study Protocol and Key Outputs
Bibliographic record
Abstract
Background: Essential health services are a package of services critical to improve health outcomes. COVID-19 pandemic disrupts essential health services. However, the level of essential health service disruption due to COVID-19 in Ethiopia is not clear. This study aimed at measuring the status of delivery of essential health services in Ethiopia during COVID-19. Methods: A national mixed-methods cross-sectional survey was conducted. It was undertaken in Amhara (10 districts), Oromia (eight districts), Sidama (six districts), Southern Nations, Nationalities, and People's Region (16 districts), and Dire Dawa City Administration. A total of 452 health facilities were surveyed. Data were collected using face-to-face interview. Descriptive analysis was undertaken. Qualitative data was analyzed thematically. Results: The woredas (districts) and health facilities which adopted essential health services before the COVID-19 pandemic were 81.4% and 51.2%, respectively. Nearly all health centers provided antenatal care services. Blood pressure measuring apparatus and delivery set were available in all health centers. However, only 50% of health centers had radiant warmer. Malnutrition services were provided by 47% of rural health centers. Moreover, a functional incinerator was available in only 41% of health centers. The provision of cardiovascular disease management was at 27.2%. Furthermore, HIV/AIDS treatment was provided by 43.5% of health facilities. Conclusion: The adoption of lists of essential health services was optimal. The status of delivery of essential health services was high for maternal healthcare. Neonatal care at birth, malnutrition treatment, and cardiovascular disease management were low. The district health system should strive more to maintain essential health services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.005 |
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".