Reaching the dream of optimal development for every child, everywhere: what do we know about ‘how to’?
Bibliographic record
Abstract
WHY NOW?Early child development (ECD) is fundamental for the health, well-being and life opportunities of every child, everywhere. 1 2 It is central to many Sustainable Development Goals (SDGs) and the global child health redesign process, led by WHO and UNICEF.[1][2][3] A strong investment case for ECD has been made by academics, as well as large intergovernment investment platforms including G20 and the World Bank. 1 3The Nurturing Care Framework, launched in May 2018, provides a policy roadmap for multiple sectors to enable a world where families and communities can support their children's developmental needs including health, nutrition, safety and security, responsive care and opportunities for early learning.4 There is a growing evidence base that inputs especially from preconception to 2 years of age, can improve cognitive, motor, language and socioemotional developmental outcomes, although studies are still small scale and short term. 2 However, there is a major gap in evidence-based guidance on how to implement at scale, especially in low-and middle-income countries (LMIC).5 While policymakers may now be committed to investing for ECD, they face unanswered questions about what, where and how to scale in programmes and especially how to measure progress.Paediatricians and child health workers are well placed to reach the youngest children through routine health systems; yet, they similarly face challenges in considering where to start, what to do, and how to reach the most vulnerable.Parents, caregivers and communities are also key to involve in programme design.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.012 | 0.030 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".