The implementation of a maternal mHealth project in South Africa: Lessons for taking mHealth innovations to scale
Bibliographic record
Abstract
MomConnect is a mHealth programme in South Africa targeted at improving antenatal and maternal health. This study explored the barriers and facilitators to implementing the MomConnect programme in South Africa and the applicable lessons for scaling mHealth programmes in Africa. We reviewed the published literature and collected data through semi-structured interviews of project partners and leaders who worked on the MomConnect project. We asked study participants to identify any barriers and/or facilitators in the implementation of the MomConnect project and how they would overcome those barriers and strengthen the facilitators. We employed the Consolidated Framework for Implementation Research (CFIR) to inform the identification of a priori codes for the directed content analysis. Based on the analysis, the key components that supported the implementation of the MomConnect project included (1) strategic partnership and coordination across partner levels, (2) cost-effective technology and sustainable funding measures, (3) adequate adaptation of the innovation to local and national settings, and (4) guiding mHealth policy and legislation frameworks. The study’s results suggest that strong political will and a robust partnership are essential to lead the strategic implementation process of mHealth projects. Such mHealth projects thrive in a policy framework that can support the planning and implementation process. In addition, understanding the accessibility needs of the project’s target population as identified in this study is critical to developing mHealth projects that are sustainable in the long term by adapting to cost-effective technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".