Unpacking the challenges of fragmentation in community-based maternal newborn and child health and health system in rural Ethiopia: A qualitative study
Bibliographic record
Abstract
INTRODUCTION: In Ethiopia, country-wide community-based primary health programs have been in effect for about two decades. Despite the program's significant contribution to advancing Maternal Newborn and Child Health (MNCH), Ethiopia's maternal and child mortality is still one of the highest in the world. The aim of this manuscript is to critically examine the multifaceted fragmentation challenges of Ethiopia's Community Health Workers (CHWs) program to deliver optimum MNCH and build a resilient community health system. METHODS: We conducted a qualitative case study in West Shewa Zone, rural Ethiopia. A purposive sampling technique was used to recruit participants. Data sources were two focus group discussions with sixteen CHWs, twelve key informant interviews with multilevel public health policy actors, and a policy document review related to the CHW program to triangulate the findings. Thematic analysis of the qualitative data was conducted. The World Health Organization's health systems framework and socio-ecological model guided the data collection, analysis, and interpretation. RESULTS: The CHWs program has been an extended arm of Ethiopia's primary health system and has contributed to improved health outcomes. However, the program has been facing unique systemic challenges that stem from the fragmentation of health finance; medical and equipment supply; working and living infrastructures; CHWs empowerment and motivation, monitoring, supervision, and information; coordination and governance; and community and stakeholder engagement. The ongoing COVID-19 and volatile political and security issues are exacerbating these fragmentation challenges. CONCLUSION: This study emphasized the gap between the macro (national) level policy and the challenge during implementation at the micro (district)level. Fragmentation is a blind spot for the community-based health system in rural Ethiopia. We argue that the fragmentation challenges of the community health program are exacerbating the fragility of the health system and fragmentation of MNCH health outcomes. This is a threat to sustain the MNCH outcome gains, the realization of national health goals, and the resilience of the primary health system in rural Ethiopia. We recommend that beyond the current business-as-usual approach, it is important to emphasize an evidence-based and systemic fragmentation monitoring and responsive approach and to better understand the complexity of the community-based health system fragmentation challenges to sustain and achieve better health outcomes. The challenges can be addressed through the adoption of transformative and innovative approaches including capitalizing on multi-stakeholder engagement and health in all policies in the framework of co-production.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".