Newborn Care Corner: A Simplified Approach to Providing Optimal Newborn Care Immediately After Birth: Newborn Care Corner
Bibliographic record
Abstract
Introduction: In 2005, a quarter of all child deaths in Ethiopia happened in the neonatal period, with one quarter of them caused by perinatal asphyxia. The proportion of neonates receiving essential newborn care, including basic life support was alarmingly low. In response to this lack of care, a new and innovative Newborn Care Corner approach was established. The primary objective of establishing a NBC near the labor and delivery room of health centers and primary hospitals was to ensure that all steps of essential newborn care are provided as one clinical bundle to all neonates, including resuscitation of asphyxiated neonates within the first few minutes of life. Objective: To assess the impact and feasibility of the NBC approach, Methods: One hundred resuscitation units were locally manufactured and piloted in 100 health facilities. Results: The pilot demonstrated that health workers could effectively resuscitate neonates and save more than 95% of asphyxiated babies in their respective facilities. As a result, Ethiopia procured approximately 2,000 standard neonatal resuscitation units to move toward full-scale national implementation.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".