Water and sanitation infrastructure for health: The impact of foreign aid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The accessibility to improved water and sanitation has been understood as a crucial mechanism to save infants and children from the adverse health outcomes associated with diarrheal disease. This knowledge stimulated the worldwide donor community to develop a specific category of aid aimed at the water and sanitation sector. The actual impact of this assistance on increasing population access to improved water and sanitation and reducing child mortality has not been examined. METHODS: We performed a country-level analysis of the relationship between water and sanitation designated official development assistance (WSS-ODA) per capita, water and sanitation coverage, and infant and child mortality in low-income countries as defined by the World Bank. We focused our inquiry to aid effectiveness since the establishment of the Millennium Development Goals (MDGs). RESULTS: Access to improved water has consistently improved since 2002. Countries receiving the most WSS-ODA ranged from odds ratios of 4 to 18 times more likely than countries in the lowest tertile of assistance to achieve greater gains in population access to improved water supply. However, while there were modestly increased odds of sanitation access, these were largely non-significant. The countries with greatest gains in sanitation were 8-9 times more likely to have greater reductions in infant and child mortality. CONCLUSIONS: Official development assistance is importantly impacting access to safe water, yet access to improved sanitation remains poor. This highlights the need for decision-makers to be more intentional with allocating WSS-ODA towards sanitation projects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it