Countdown to 2015 country case studies: what can analysis of national health financing contribute to understanding MDG 4 and 5 progress?
Bibliographic record
Abstract
BACKGROUND: Countdown to 2015 (Countdown) supported countries to produce case studies that examine how and why progress was made toward the Millennium Development Goals (MDGs) 4 and 5. Analysing how health-financing data explains improvements in RMNCH outcomes was one of the components to the case studies. METHODS: This paper presents a descriptive analysis on health financing from six Countdown case studies (Afghanistan, Ethiopia, Malawi, Pakistan, Peru, and Tanzania), supplemented by additional data from global databases and country reports on macroeconomic, health financing, demographic, and RMNCH outcome data as needed. It also examines the effect of other contextual factors presented in the case studies to help interpret health-financing data. RESULTS: Dramatic increases in health funding occurred since 2000, where the MDG agenda encouraged countries and donors to invest more resources on health. Most low-income countries relied on external support to increase health spending, with an average 20-64 % of total health spending from 2000 onwards. Middle-income countries relied more on government and household spending. RMNCH funding also increased since 2000, with an average increase of 119 % (2005-2010) for RMNH expenditures (2005-2010) and 165 % for CH expenditures (2005-2011). Progress was made, especially achieving MDG 4, even with low per capita spending; ranging from US$16 to US$44 per child under 5 years among low-income countries. Improvements in distal factors were noted during the time frame of the analysis, including rapid economic growth in Ethiopia, Peru, and Tanzania and improvements in female literacy as documented in Malawi, which are also likely to have contributed to MDG progress and achievements. CONCLUSIONS: Increases in health and RMNCH funding accompanied improvements in outcomes, though low-income countries are still very reliant on external financing, and out-of-pocket comprising a growing share of funds in middle-income settings. Enhancements in tracking RMNCH expenditures across countries are still needed to better understand whether domestic and global health financing initiatives lead to improved outcomes as RMNCH continues to be a priority under the Sustainable Development Goals.
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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 | 0.042 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".