Improving Access to and Delivery of Maternal Health Care Services to Prevent Postpartum Hemorrhage in Selected States in Nigeria: Human-Centered Design Study
Bibliographic record
Abstract
Background: A significant cause of postpartum hemorrhage (PPH) is access to and delivery of maternal health care services. Several multisectoral strategies have been deployed to address the challenges with little success, thereby necessitating the use of human-centered design (HCD) to enhance health care delivery, particularly in PPH management. Objective: This study aims to develop facility-level solutions for optimizing uterotonic supply chain systems and health service delivery in PPH management through an HCD approach in selected Nigerian states. Methods: The research used a four-phase HCD methodology: (1) co-research, (2) co-design, (3) co-refinement, and (4) implementation. However, this paper focused on the first 3 phases. In the co-research phase, 203 interviews were conducted, involving 80 pregnant women and nursing mothers, 97 health care workers, and 26 key stakeholders. Additionally, 33 sites were observed across a 3-level continuum of care. Interviews and focus group discussions revealed insights into the distribution of health workers and observed PPH cases, alongside knowledge and administration of uterotonics. Data analysis was carried out using three key steps: (1) identifying key themes from the collected data, (2) developing insight statements that encapsulate these themes, and (3) translating each insight statement into actionable design opportunities. Results: About 150 ideas were produced and translated into 12 solution prototypes in the co-design phase. Progressive refinement following feedback from 140 stakeholders led to the selection of three final solutions: (1) implementing a referral linkage system to improve the transportation of pregnant women to nearby health facilities, (2) increasing demand for antenatal care services among pregnant women and their families, and (3) delivering a comprehensive uterotonic logistics management program for streamlined uterotonic storage and management. Conclusions: This approach aligns with global health trends advocating for HCD integration in health care programming and aims to empower local champions to drive sustainable improvements in maternal health outcomes. Judicious implementation of the developed prototypes across the states can strengthen clinical care and potentially reduce maternal health service delivery gaps.
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 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 | 0.023 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".