Embedding Community-Based Newborn Care in the Ethiopian health system: lessons from a 4-year programme evaluation
Bibliographic record
Abstract
Despite remarkable gains, improving neonatal survival globally remains slow paced. Innovative service-delivery packages have been developed for community health workers (CHWs) to maximize system efficiency and increase the reach of services. However, embedding these in health systems needs structural and procedural alignment. The Community-Based Newborn Care (CBNC) programme was a response to high neonatal mortality in Ethiopia. Key aspects include simplified treatment for neonatal illness, integrated outreach services and task-shifting. Using the CHW functionality model by WHO, this study evaluates the health system response to the programme, including quality of care. A before-and-after study was conducted with three survey time points: baseline (November 2013), midline (December 2015) and follow-up (December 2017-4 years after the programme started). Data were collected at a sample of primary healthcare facilities from 101 districts across four regions. Analysis took two perspectives: (1) health system response, through supplies, infrastructure support and supervision, assessed through interviews and observations at health facilities and (2) quality of care, through CHWs' theoretical capacity to deliver services, as well as their performance, assessed through functional health literacy and direct observation of young infant case management. Results showed gains in services for young infants, with antibiotics and job aids available at over 90% of health centres. However, services at health posts remained inadequate in 2017. In terms of quality of care, only 37% of CHWs correctly diagnosed key conditions in sick young infants at midline. CHWs' functional health literacy declined by over 70% in basic aspects of case management during the study. Although the frequency of quarterly supportive supervision visits was above 80% during 2013-2017, visits lacked support for managing sick young infants. Infrastructure and resources improved over the course of the CBNC programme implementation. However, embedding and scaling up the programme lacked the systems-thinking and attention to health system building-blocks needed to optimize service delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".