Strengthening Primary Care With a Minimal Digital Ecosystem in Burkina Faso: Protocol for a Pragmatic Mixed Methods Implementation Study
Bibliographic record
Abstract
Abstract Background In Burkina Faso, the Minimal Digital Ecosystem (MDE)—a suite of 9 integrated digital tools—was introduced to support key health system functions, including care delivery, financial management, medication oversight, governance, and data use. However, evidence regarding the maturity of its real-world implementation and the determinants influencing its adoption remains scarce. Objective This pragmatic mixed methods study aims to (1) measure MDE implementation maturity across 4 dimensions (adoption, fidelity, penetration, sustainability), (2) identify multilevel determinants using the Consolidated Framework for Implementation Research (CFIR 2.0) and Normalization Process Theory (NPT), and (3) examine associations between implementation degree and primary health care (Centre de Santé et de Promotion Sociale [CSPS]) performance Methods We use a sequential explanatory design (quantitative → qualitative) in 4 districts covering 72 CSPSs. Phase 1 involves a cross-sectional survey of all eligible health workers, facility managers, and community health workers (estimated 612 respondents nested within facilities) using CFIR- and NPT-informed questionnaires. Following psychometric validation (exploratory/confirmatory factor analysis; reliability assessment via Cronbach α and McDonald ω), we will fit multilevel models with CSPS random intercepts and district fixed effects to (1) quantify between-facility implementation variance, (2) test associations with CFIR and NPT determinants, and (3) examine relationships with CSPS performance indicators. Phase 2 involves purposive sampling of facilities with varying implementation profiles for interviews, focus groups, and observations, analyzed using reflexive thematic analysis to explain quantitative patterns. Results Ethics approval was obtained from Burkina Faso’s National Ethics Committee (number 2023-06-136). The study was funded in June 2022 (Gates Foundation, Grant INV-056021) and is being conducted across 72 primary health care facilities in 4 districts (Manga, Sapouy, Ténado, and Ziniaré) in Burkina Faso. Qualitative data collection commenced in November 2025 and was completed in January 2026. Quantitative data collection took place from January 2023 to October 2025. As of May 2026, qualitative data collection was completed, and quantitative analyses had commenced. Psychometric analyses are underway. Conclusions By addressing key evidence and measurement gaps in digital health implementation, this protocol will (1) generate context-specific guidance on implementing and institutionalizing a complex digital health ecosystem and (2) provide validated, ready-to-use instruments to quantify implementation at the CSPS level. Together, these outputs will help policymakers and program managers define and track “implementation success,” informing—and accelerating—the scale-up and optimization of the MDE in resource-constrained settings.
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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 | 0.072 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.086 | 0.013 |
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".