Critical maternal health knowledge gaps in low- and middle-income countries for the post-2015 era
Bibliographic record
Abstract
Effective interventions to promote maternal health and address obstetric complications exist, however 800 women die every day during pregnancy and childbirth from largely preventable causes and more than 90% of these deaths occur in low and middle income countries (LMIC). In 2014, the Maternal Health Task Force consulted 26 global maternal health researchers to identify persistent and critical knowledge gaps to be filled to reduce maternal morbidity and mortality and improve maternal health. The vision of maternal health articulated was comprehensive and priorities for knowledge generation encompassed improving the availability, accessibility, acceptability, and quality of institutional labor and delivery services and other effective interventions, such as contraception and safe abortion services. Respondents emphasized the need for health systems research to identify models that can deliver what is known to be effective to prevent and treat the main causes of maternal death at scale in different contexts and to sustain coverage and quality over time. Researchers also emphasized the development of tools to measure quality of care and promote ongoing quality improvement at the facility, district, and national level. Knowledge generation to improve distribution and retention of healthcare workers, facilitate task shifting, develop and evaluate training models to improve "hands-on" skills and promote evidence-based practice, and increase managerial capacity at different levels of the health system were also prioritized. Interviewees noted that attitudes, behavior, and power relationships between health professionals and within institutions must be transformed to achieve coverage of high-quality maternal health services in LMIC. The increasing burden of non-communicable diseases, urbanization, and the persistence of social and economic inequality were identified as emerging challenges that require knowledge generation to improve health system responses and evaluate progress. Respondents emphasized evaluating effectiveness, feasibility, and equity impacts of health system interventions. A prominent role for implementation science, evidence for policy advocacy, and interdisciplinary collaboration were identified as critical areas for knowledge generation to improve maternal health in the post-2015 era.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".