Integrating WHO’s digital adaptation kit for antenatal care into BornFyne-PNMS: insights from Cameroon
Bibliographic record
Abstract
Background: Digital health innovations represent unique opportunities to address maternal, newborn, and child health challenges in Sub-Saharan Africa. In 2021, the World Health Organization (WHO) launched the Digital Adaptation Kits (DAKs) for antenatal care (ANC) as part of its Standards-Based, Machine-Readable, Adaptive, Requirements-Based, and Testable (SMART) guidelines approach. DAKs are operational and software-neutral mechanisms that convert WHO guidelines into standardized formats that can be easily integrated into digital systems by various countries. This article outlines the methodology for updating and integrating WHO DAK content into the BornFyne-prenatal management system (PNMS) version 2.0. Methods: This study, which employs a participatory action research approach, is part of a larger research study for the BornFyne-PNMS project. A review of the ANC DAK operational document and data dictionaries was conducted to identify elements that were present in BornFyne-PNMS version 1.0. This was followed by a series of consultations and stakeholder meetings. Results: Five stakeholder meetings were held to engage stakeholders across Cameroon. Some of the registration elements, among other DAK aspects of ANC service provision, were identified in BornFyne version 1.0 but required reorganizing, remodeling, and reanalyzing to align with the International Classification of Diseases codes and DAK data content as part of the expansion for BornFyne version 2.0. Up to 40% of the DAK dictionary data content existed within the BornFyne-PNMS version 1.0, including additional DAK content adapted to update BornFyne-PNMS version 2.0. The digital health ecosystem in Cameroon is in an emerging phase with an increasing demand for digital health technologies, especially in the areas of reproductive, maternal, newborn, child, and adolescent health. Conclusion: The digital health ecosystem in Cameroon is in an emerging phase with an increasing demand for digital health technologies, especially in the area of reproductive, maternal, newborn, child, and adolescent health. This article describes and documents the steps in operationalization of the ANC DAK content into the BornFyne-PNMS content, highlighting the DAK as an important tool for guiding and facilitating software engineers in developing and integrating recommended ANC guidelines into digital platforms to facilitate interoperability, going by the structure of the document, its workflow processes, and content mapping elements.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".