Developing the BornFyne prenatal management system version 2.0: a mixed method community participatory approach to digital health for reproductive maternal health
Bibliographic record
Abstract
Despite the growing number of global initiatives aimed at reducing adverse maternal health outcomes, there remain critical gaps and disparities in access to maternal health services in Cameroon and across the sub-Saharan Africa. Digital health innovations represent unique opportunities for addressing maternal and newborn child health in sub-Saharan Africa. This article documents the approach to developing the BornFyne-Prenatal Management System (PNMS) as an intervention to support maternal health issues in Cameroon. The mixed-method design employed the three-delays model conducted in four health districts purposefully selected with a mix of urban and rural settings as defined in the context. The study employed focus group discussions and interviews to inform the development features. A total of 25 providers were interviewed, 12 focus group discussions and 4 workshops were held and a total of 3654 households were surveyed. Participants highlighted multifaceted advantages of using digital health platform such as BornFyne-PNMS to enhance communication and care during pregnancy such as remote consultations, emergency response, increased patient engagement and improved continuity of care and convenience. Most respondents believed that the use of a digital platform like BornFyne-PNMS would greatly facilitate access to health facilities, especially during emergencies. The BornFyne-PNMS deployment includes community engagement, training and practical skills building of health workers in the use of digital technologies, the establishment of an emergency transport mechanism for response to emergency cases, assessment and upgrading of the computer hardware of enrolled health facilities and support to health system managers to review and interpret the BornFyne data and interoperability with the national health management information system.
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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 | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".