Community participation for reproductive, maternal, newborn and child health: insights from the design and implementation of the BornFyne-prenatal management system digital platform in Cameroon
Bibliographic record
Abstract
Introduction: Across communities in low-middle income countries, digital health is currently revolutionizing the delivery of health services, particularly in the field of reproductive, maternal, newborn, and child health (RMNCH) services. While studies have shown the effectiveness of mHealth in delivering RMNCH services, there is little information about factors that enhance mHealth services utilization in low-cost settings including stakeholders' level of influence on the implementation of digital health intervention in sub-Saharan Africa. This paper seeks to describe important lessons on the levels of stakeholders' direct or indirect influence on the design and implementation of the BornFyne-PNMS digital health platform to support RMNCH services. Methods: A participatory research (PR) design approach was employed to explore stakeholders' perspectives of a new initiative, through direct engagement of local priorities and perspectives. The process of introducing the digital application called the BornFyne-PNMS for district health delivery system and the community, and integrating it within the district health delivery system was guided by research-to-action, consistent with the PR approach. To explore stakeholders' perspectives through a PR approach, we conducted a series of stakeholder meetings fashioned after focus group discussions. Results: Issues around male involvement in the program, sensitization and equity concerns arose. Emergent challenges and proposed strategies for implementation from diverse stakeholders evidently enriched the design and implementation process of the project intervention. Stakeholder meetings informed the addition of variables on the mobile application that were otherwise initially omitted, which will further enhance the RMNCH electronic data collection for health information systems strengthening in Cameroon. Discussion: This study charts a direction that is critical in digital health delivery of RMNCH in a rural and low-income community and describes the important iterative stakeholder input throughout the study. The strategy of stakeholders' involvement in the BornFyne PNMS implementation charts a direction for ownership and sustainability in the strengthening of Cameroon's health information system.
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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 | 0.013 | 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 | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".