Using human-centered design to re-vision the emergency obstetric and newborn care framework: Insights from Bangladesh, Malawi and Senegal
Bibliographic record
Abstract
The Emergency Obstetric and Newborn Care (EmONC) Framework has been instrumental in helping countries plan and monitor maternal health services for over 20 years. Given evolving health systems, the changing expectations of both patients and health providers, and the expanded evidence base, the "Re-Visioning EmONC" project was initiated to update this framework to better meet countries' needs. To understand the needs of its primary intended users, the project used human-centered design (HCD) to conduct in-depth studies in three countries with extensive experience using the EmONC Framework: Bangladesh, Malawi, and Senegal. The study employed HCD methods to conduct interviews, focus groups, and consultative workshops with 337 participants (e.g., health managers, health providers, and service users) across the three countries. Each country team developed their own themes to explore within the boundaries of the overall Re-Visioning EmONC project's global objectives and developed unique generative activities based on the primary research questions and the category of participants. Multi-stage data analysis was conducted using affinity diagrams and either atlas.ti or Nvivo. Seven key cross-country insights emerged that together can be summarized as follows: when health systems fail, the burden of accessing and providing EmONC shifts to individuals - women, families, and health providers - who must improvise solutions, leading not only to poor quality of care, but also to huge out of pocket expenses, poor wellbeing and a profound mistrust in each other and in the system. The insights informed revisions to the EmONC Framework, including enhanced guidance on context-specific planning, new indicators for facility readiness, incorporation of workforce wellbeing, and increased focus on integrated maternal-newborn care. The HCD approach enabled meaningful integration of user perspectives into the revised EmONC Framework. The revised framework provides a roadmap for strengthening health systems and improving outcomes for women with obstetric complications and small and sick newborns.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".