What can work and how? An overview of evidence-based interventions and delivery strategies to support health and human development from before conception to 20 years
Bibliographic record
Abstract
Progress has been made globally in improving the coverage of key maternal, newborn, and early childhood interventions in low-income and middle-income countries, which has contributed to a decrease in child mortality and morbidity. However, inequities remain, and many children and adolescents are still not covered by life-saving and nurturing care interventions, despite their relatively low costs and high cost-effectiveness. This Series paper builds on a large body of work from the past two decades on evidence-based interventions and packages of care for survival, strategies for delivery, and platforms to reach the most vulnerable. We review the current evidence base on the effectiveness of a variety of essential and emerging interventions that can be delivered from before conception until age 20 years to help children and adolescents not only survive into adulthood, but also to grow and develop optimally, support their wellbeing, and help them reach their full developmental potential. Although scaling up evidence-based interventions in children younger than 5 years might have the greatest effect on reducing child mortality rates, we highlight interventions and evidence gaps for school-age children (5-9 years) and the transition from childhood to adolescence (10-19 years), including interventions to support mental health and positive development, and address unintentional injuries, neglected tropical diseases, and non-communicable diseases.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".