Building a Stronger System for Tracking Nutrition-Sensitive Spending
Bibliographic record
Abstract
BACKGROUND: There is growing awareness that the necessary solutions for improving nutrition outcomes are multisectorial. As such, investments are increasingly directed toward "nutrition-sensitive" approaches that not only address an underlying or basic determinant of nutrition but also seek to achieve an explicit nutrition goal or outcome. Understanding how and where official development assistance (ODA) for nutrition is invested remains an important but complex challenge, as development projects components vary in their application to nutrition outcomes. Currently, no systematic method exists for tracking nutrition-sensitive ODA. OBJECTIVE: To develop a methodology for classifying and tracking nutrition-sensitive ODA and to produce estimates of the amount of nutrition-sensitive aid received by countries with a high burden of undernutrition. METHODS: We analyzed all financial flows reported to the Organization for Economic Co-Operation and Development's Development Assistance Committee Creditor Reporting Service in 2010 to estimate these investments. We assessed the relationships between national stunting prevalence, stunting burden, under-5 mortality, and the amount of nutrition-specific and nutrition-sensitive ODA. RESULTS: We estimate that, in 2010, a total of $379·4 million (M) US dollars (USD) was committed to nutrition-specific projects and programs of which 25 designated beneficiaries (countries and regions) accounted for nearly 85% ($320 M). A total of $1.79 billion (B) was committed to nutrition-sensitive spending, of which the top 25 countries/regions accounted for $1.4 B (82%). Nine categories of development activities accounted for 75% of nutrition-sensitive spending, led by Reproductive Health Care (30·4%), Food Aid/Food Security Programs (14·1%), Emergency Food Aid (13·2%), and Basic Health Care (5·0%). Multivariate linear regression models indicate that the amount of nutrition-sensitive (P = .001) and total nutrition ODA was significantly predicted by stunting prevalence (P = .001). The size of the total population of stunted children significantly predicted the amount of nutrition-specific ODA (P < .001). CONCLUSION: The recipient profile of nutrition-specific and nutrition-sensitive ODA is related but distinct. Nutrition indicators are associated with the level of nutrition-related ODA commitments to recipient countries. A reliable estimate of nutrition spending is critical for effective planning by both donors and recipients and key for success, as the global development community recommits to a new round of goals to address the interrelated causes of undernutrition in low-income countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".