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Record W4411551074 · doi:10.1371/journal.pgph.0004771

Using human-centered design to re-vision the emergency obstetric and newborn care framework: Insights from Bangladesh, Malawi and Senegal

2025· article· en· W4411551074 on OpenAlexaff
Isabelle Moreira, Elizabeth Chodzaza, Mahbub Elahi Chowdhury, Thierno Dieng, Rasheda Khan, Sylvain Landry Faye, Kaosar Afsana, Martha Kamanga, Chifundo Zimba, Emas Potolani, Kate Ramsey, Lynn P. Freedman, Caitlin Warthin, Samantha Lobis

Bibliographic record

VenuePLOS Global Public Health · 2025
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsImpact
FundersUnited Nations Population FundBill and Melinda Gates Foundation
KeywordsMedicineGeographyMedical emergency

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.094
GPT teacher head0.374
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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