Improving humanitarian health responses for children through nurturing care
Bibliographic record
Abstract
Children are disproportionately impacted by humanitarian disasters, which cause toxic stress. When a crisis overwhelms the capacity of health and social systems to meet the needs of a population, external crisis response teams working in a range of sectors may offer support to save lives and meet the affected populations' basic needs. Gaps have been identified in health sector interventions for children in humanitarian contexts, including lack of routine interventions to protect and promote early child development (ECD). To address this gap and improve the quality of humanitarian responses for girls and boys, the Global Health Cluster, Child Health Task Force, and the Inter-Agency Network for Education in Emergencies held a webinar series on Strengthening Nurturing Care in Humanitarian Response. It concluded that incorporating interventions to support nurturing care for ECD into health responses in acute phase emergencies is lifesaving. In crisis contexts, even simple interventions can be the difference between life and death, and when systematically applied, they can dramatically improve a child's life opportunities as well as national recovery and economic growth.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".