Scaling Mhealth in Africa: Lessons From The Implementation of The MomConnect Program
Bibliographic record
Abstract
Abstract Background: Mobile health programs have strengthened health systems in Low- and Middle-Income Countries (LMICs) to achieve health-related goals. MomConnect, a mobile health program in South Africa targeted at improving antenatal and maternal health, has scaled rapidly since its creation in 2014. This study explores the barriers and facilitators to the implementation and scaling of the MomConnect program and the applicable lessons for the scaling of mhealth programs in the region. Methods: We conducted a qualitative study with key project partners and leaders who worked on the MomConnect project. Interviewees were initially identified through a literature review, publications, and evaluations of the project. Interviewees included individuals serving in implementation oversight, champions, partners, funders and frontline implementer roles. The Consolidated Framework for Implementation Research (CFIR) informed the a priori codes for directed content analysis. In total, 15 key stakeholders were interviewed. Interviewees were asked to identify any barriers or facilitators to the implementation of MomConnect and how they would overcome those barriers and strengthen the facilitators. Results: This qualitative study identified multiple barriers and facilitators to implementation within our domain of CFIR: characteristics of the intervention (complexity, trialability, evidence strength & quality, cost, design quality & packaging, adaptability), inner setting (available resources, compatibility, implementation climate, access to knowledge & information), outer setting (cosmopolitanism, external policy & incentives) and process (planning, external change agents, champions, formally appointed internal implementation leaders). Overarching thematic areas spanning the barriers and facilitators included: (1) strategic partnership and coordination across multiple sectors, (2) innovation costs and funding, (3) operationalization of the innovation to local and national settings and (4) mhealth policy and legislation frameworks.Conclusion: The barriers and facilitators identified under the CFIR domains can be used to build knowledge on how to strengthen mhealth programs in Africa. The continued success of the MomConnect program will require overcoming identified barriers and capitalizing on known facilitators. These findings can serve as a foundation for the effective design and scale of mhealth interventions in the region.
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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 | 0.036 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".