The role of intersectoral collaboration and continuous stakeholder engagement in the implementation of the BornFyne PNMS project in Cameroon
Bibliographic record
Abstract
Background: The process of stakeholder engagement in the implementation of a digital health platform is vital, especially in low-resourced countries like Cameroon, where the digital health ecosystem is still emerging. Stakeholder engagement can make meaningful contributions to a project allowing for increased project visibility and reach, uptake, acceptability, and sustainability. However, collaboration among stakeholders during stakeholder engagement cannot be implied. This article focuses solely on intersectoral collaboration amongst stakeholders in the implementation of the BornFyne-PNMS digital health platform in Cameroon. Method: The study took on a participatory action research approach using stakeholder discussions, feedback from participants, questions, and suggestions to inform the progress and continuous implementation. This also included follow-up discussions with stakeholders from various sectors. The intersectoral meetings took into consideration a wider perspective on the implementation process and the launch of universal health coverage in Cameroon. Results: A total of five stakeholder meetings were held during the implementation phase, and a total of 174 stakeholders were engaged. As a follow-up, a total of 19 letters were prepared by the Department of Family Health and addressed to strategic departments including international partners to introduce the BornFyne project. Findings are centered on six major themes that emerged from the coded data and they are as follows: (1) aligned goals and objectives; (2) enhanced health care delivery; (3) data quality and availability; (4) accessibility issues; (5) intersectoral collaboration for universal health coverage; and (6) equity aspects. Conclusion: This article underscores the relevance of engaging a diverse group of stakeholders as a strength in intersectoral collaboration and partnership in implementing digital health interventions. It ensures that the views and experiences of those directly impacted by the intervention are considered, and it contributes to a more well-rounded and impactful assessment of the BornFyne-PNMS platform's role in improving RMNCAH in rural settings.
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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 | 0.054 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| 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".