Donor financing of human resources for health, 1990–2016: an examination of trends, sources of funds, and recipients
Bibliographic record
Abstract
BACKGROUND: Skilled health professionals are a critical component of the effective delivery of lifesaving health interventions. The inadequate number of skilled health professionals in many low- and middle-income countries has been identified as a constraint to the achievement of improvements in health outcomes. In response, more international development agencies have provided funds toward broader health system initiatives and health workforce activities in particular. Nonetheless, estimates of the amount of donor funding targeting investments in human resources for health activities are few. METHODS: We utilize data from the Institute for Health Metrics and Evaluation's annual database on development assistance for health. The estimates in the database are generated using data from publicly available databases that track development assistance. To estimate development assistance for human resources for health, we use keywords to identify projects targeted toward human resource processes. We track development for human resources for health from 1990 through 2016. We categorize the types of human-resources-related projects funded and examine the availability of human resources, development assistance for human resources for health, and disease burden. RESULTS: We find that the amount of donor funding directed toward human resources for health has increased from only $34 million in 1990 to $1.5 billion in 2016 (in 2017 US dollars). Overall, $18.5 billion in 2017 US dollars was targeted toward human resources for health between 1990 and 2016. The primary regions receiving these resources were sub-Saharan Africa and Southeast Asia, East Asia, and Oceania. The main donor countries were the United States, Canada, Australia and the United Kingdom. The main agencies through which these resources were disbursed are non-governmental organizations (NGOs), US bilateral agencies, and UN agencies. CONCLUSION: In 2016, less than 4% of development assistance for health could be tied to funding for human resources. Given the central role skilled health workers play in health systems, in order to make credible progress in reducing disparities in health and attaining the goal of universal health coverage for all by 2030, it may be appropriate for more resources to be mobilized in order to guarantee adequate manpower to deliver key health interventions.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".