Health system strengthening using a Maximizing Engagement for Readiness and Impact (MERI) Approach: A community case study
Bibliographic record
Abstract
Introduction Health system strengthening initiatives in low and middle-income countries are commonly hampered by limited implementation readiness. The Maximizing Engagement for Readiness and Impact (MERI) Approach uses a system “readiness” theory of change to address implementation obstacles. MERI is documented based on field experiences, incorporating best practices, and lessons learned from two decades of maternal, newborn, and child health (MNCH) programming in East Africa. Context The MERI Approach is informed by four sequential and progressively larger MNCH interventions in Uganda and Tanzania. Intervention evaluations incorporating qualitative and quantitative data sources assessed health and process outcomes. Implementer, technical leader, stakeholder, and policymaker reflections on sequential experiences have enabled MERI Approach adaptation and documentation, using an implementation lens and an implementation science readiness theory of change. Key programmatic elements The MERI Approach comprises three core components. MERIChange Strategies (meetings, equipping, training, mentoring) describe key activity types that build general and intervention-specific capacity to maximize and sustain intervention effectiveness. The SOPETAR ProcessModel (Scan, Orient, Plan, Equip, Train, Act, Reflect) is a series of purposeful steps that, in sequence, drive each implementation level (district, health facility, community). A MERIMotivational Framework identifies foundational factors (self-reliance, collective-action, embeddedness, comprehensiveness, transparency) that motivate participants and enhance intervention adoption. Components aim to enhance implementer and system readiness while engaging broad stakeholders in capacity building activities toward health outcome goals. Activities align with government policy and programming and are embedded within existing district, health facility, and community structures. Discussion This case study demonstrates feasibility of the MERI Approach to support district wide MNCH programming in two low-income countries, supportive of health outcome and health system improvements. The MERI Approach has potential to engage districts, health facilities, and communities toward sustainable health outcomes, addressing intervention implementation gaps for current and emerging health needs within and beyond East Africa.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".