Home Births and Limited Access to
Bibliographic record
Abstract
Until about twenty years ago, child survival meant the survival of children rather than newborn infants. With a steady worldwide decline in under-5 deaths— most of the lives saved being those of infants and children over the age of a month—the newborn period has come into focus as a relatively intransigent source of mortality. The ‘‘child survival revolution’’ increased child survival [1], but newborn infants went largely unnoticed. Neonatal mortality (0–28 d) now accounts for about two-thirds of global infant (0–1 y) mortality and about 3.8 million of the 8.8 million annual deaths of children under 5 [2]. Most of these deaths (98%) occur in low- and middle-income countries [3]. The last two decades have seen a rise in advocacy—a call for attention to the newborn infant along with her mother and siblings—and an incremental growth in the evidence for potential interventions [4–6]. Reducing neonatal mortality is both an ethical obligation and a prerequisite to achieving Millennium Development Goal 4, the target of which is a reduction in child mortality by two-thirds between 1990 and 2015. A 2008 report found only a quarter of relevant countries on track to reach this target [7]. Immediate Challenges The main obstacles to improving newborn survival are that many babies are born at home without skilled attendance, care-seeking for maternal and newborn ailments is limited, health workers are often not skilled and confident in caring for newborn infants, and inequalities in all these factors are felt by those most in need. The Policy Forum allows health policy makers around the world to discuss challenges and opportunities for improving health care in their societies.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.112 | 0.008 |
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".