Opportunities and Challenges in Global Perinatal Research
Bibliographic record
Abstract
BACKGROUND: The global plight of stillbirths and neonatal mortality is concentrated in low- and middle-income countries. The ambitious targets introduced by the World Health Organization in the Every Newborn Action Plan demand a commitment to research that promotes equitable perinatal outcomes. OBJECTIVES: The aim of this review was to understand the opportunities for global perinatal research and the accompanying challenges. METHODS: We conducted a literature search to identify research prioritization exercises from 2014 to 2018 pertaining to global perinatal health. The top 50 questions with the highest research prioritization scores were extracted and analyzed. RESULTS: The greatest priorities centered on community-based, implementation research targeting major causes of stillbirth and neonatal mortality in low-resource settings. The priorities are saddled with prerequisite conditions, design obstacles, and ethical considerations that require attention. CONCLUSIONS: While the challenges are undeniable, the need to make the perinatal period healthier for babies worldwide has never been clearer.
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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 | 0.052 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".