How did COVID-19 impact development assistance for health? – The trend for country-specific disbursement between 2015 and 2020
Bibliographic record
Abstract
This study aimed to examine the changes that took place between 2015-2019 and 2020 and reveal how the COVID-19 pandemic affected financial contributions from donors. We used the Creditor Reporting System database of the Organization for Economic Cooperation and Development to investigate donor disbursement. Focusing on the Group of Seven (G7) countries and the Bill and Melinda Gates Foundation (BMGF), we analyzed their development assistance for health (DAH) in 2020 and the change in their disbursement between 2015 and 2020. As a result, total disbursements for all sectors increased by 14% for the G7 and the BMGF. In 2020, there was an increase in DAH for the BMGF and the G7 except for the United States. The total disbursement amount for the "COVID-19" category by G7 countries and the BMGF was approximately USD 3 billion in 2020, which was 3 times larger than for Malaria, 8.5 times larger for Tuberculosis, and 60% smaller for STDs including HIV/AIDS for the same year. In 2020 as well, the United States, the United Kingdom, Japan, Italy, and Canada saw their disbursements decline for more than half of 26 sectors. In conclusion, the impact of COVID-19 was observed in the changes in DAH disbursement for three major infectious diseases and other sectors. To consistently address the health needs of low- and middle-income countries, it is important to perform a follow-up analysis of their COVID-19 disbursements and the influence of other DAH areas.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".