Accountability for funds for Nurturing Care: what can we measure?
Bibliographic record
Abstract
BACKGROUND: Understanding donor, government and out-of-pocket funding for early child development (ECD) is important for tracking progress. We aimed to estimate a baseline for the WHO, UNICEF and World Bank Nurturing Care Framework (NCF) with a special focus on childhood disability. METHODS: To estimate development assistance spending, the Organisation for Economic Cooperation and Development's Creditor Reporting System (OECD-CRS) database was searched for 2007-2016, using key words derived from domains of the NCF (good health, nutrition and growth, responsive caregiving, security and safety, and early learning), plus disability. Associated funds were analysed by domain, donor, recipient and region. Trends of ECD/NCF were compared with reproductive, maternal, newborn and child health (RMNCH) disbursements. To assess domestic or out-of-pocket expenditure for ECD, we searched electronic databases of indexed and grey literature. RESULTS: US$79.1 billion of development assistance were disbursed, mostly for health and nutrition (US$61.9 billion, 78% of total) and least for disability (US$0.7 billion, 2% of total). US$2.3 per child per year were disbursed for non-health ECD activities. Total development assistance for ECD increased by 121% between 2007 and 2016, an average increase of 8.3% annually. Per child disbursements increased more in Africa and Asia, while minimally in Latin America and the Caribbean and Oceania. We could not find comparable sources for domestic funding and out-of-pocket expenditure. CONCLUSIONS: Estimated international donor disbursements for ECD remain small compared with RMNCH. Limitations include inconsistent donor terminology in OECD data. Increased investment will be required in the poorest countries and for childhood disability to ensure that progress is equitable.
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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 | 0.135 | 0.365 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.014 | 0.031 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| 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".