Lessons Learned From Helping Babies Survive in Humanitarian Settings
Bibliographic record
Abstract
Humanitarian crises, driven by disasters, conflict, and disease epidemics, have profound effects on society, including on people's health and well-being. Occurrences of conflict by state and nonstate actors have increased in the last 2 decades: by the end of 2018, an estimated 41.3 million internally displaced persons and 20.4 million refugees were reported worldwide, representing a 70% increase from 2010. Although public health response for people affected by humanitarian crisis has improved in the last 2 decades, health actors have made insufficient progress in the use of evidence-based interventions to reduce neonatal mortality. Indeed, on average, conflict-affected countries report higher neonatal mortality rates and lower coverage of key maternal and newborn health interventions compared with non-conflict-affected countries. As of 2018, 55.6% of countries with the highest neonatal mortality rate (≥30 per 1000 live births) were affected by conflict and displacement. Systematic use of new evidence-based interventions requires the availability of a skilled health workforce and resources as well as commitment of health actors to implement interventions at scale. A review of the implementation of the Helping Babies Survive training program in 3 refugee responses and protracted conflict settings identify that this training is feasible, acceptable, and effective in improving health worker knowledge and competency and in changing newborn care practices at the primary care and hospital level. Ultimately, to improve neonatal survival, in addition to a trained health workforce, reliable supply and health information system, community engagement, financial support, and leadership with effective coordination, policy, and guidance are required.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".