Transforming multi-stakeholder engagement towards coproduction of optimized maternal, newborn, and child health and a resilient community health system in rural Ethiopia: A qualitative study
Bibliographic record
Abstract
INTRODUCTION: In Ethiopia, Maternal, Newborn, and Child Health (MNCH) outcomes have been improving, however, the current level of Maternal and under-five children mortality remains the highest in the world. Despite the rhetoric around the significance of multi-stakeholder engagement as a buzzword in development theories and polices to improve health and other development outcomes, there is limited evidence on how multi-stakeholders intersect and mutually reinforce each other toward the coproduction of improved MNCH outcomes and a resilient community health system. The aim of this manuscript is to examine barriers to and facilitators of coproduction in the context of multi-stakeholder engagement to optimize MNCH outcomes and a resilient community health system in rural Ethiopia. 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 groups discussions with CHWs, twelve key informant interviews with multilevel public health policy actors, and a policy document review related to the CHW program to triangulate the finding. Thematic analysis of the qualitative data was conducted. Our study was informed by multiple theoretical frameworks including the World Health Organization's building block framework, state- society synergy model to inform the research processes and analysis. RESULTS: In the context of multi-stakeholder approach, our analysis revealed the multilevel barriers to and facilitators of coproduction in the community health landscape in rural Ethiopia. The major barriers of coproduction include lack of vertical and horizontal alignment, lack of continuum of and sustainable engagement practice,lack of systemic coordination platforms, and Inadequate coordination and implementation capacity. Major facilitators of coproduction include embedded integrated community health system, promising macro-level multi-stakeholder and community-level engagement and coproduction aspects. CONCLUSIONS: Our study reveals mixed policy and practice-related results, the current multi-stakeholder engagement is necessary but insufficient and fragmented to coproduce optimized MNCH outcomes and ensure a resilient health system in rural Ethiopia. Moving beyond the current multi-stakeholder engagement as a buzzword in health polices to practice through, embracing meaningful coproduction frameworks is fundamental while building on multi-stakeholder engagement efforts to optimize MNCH outcomes and a resilient community health system. A coproduction framework leverages the intersection and mutual reinforcement of multi-stakeholder synergy throughout the CHWs' program cycle through shared power and joint assessment, planning, implementing, decision making and evaluating. Fostering effective multi-stakeholder engagement synergy requires balanced shared power, alignment to community priorities, systemic mapping, coordination and monitoring, and continuum and sustainability of engagement strategies. Beyond donor initiatives and a dependency approach, proactive health diplomacy strategies are also important to sustain the existing and attract new actors to realize sustainable positive health outcomes and a resilient community health policy and strategy.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".