Donor funding health policy and systems research in low- and middle-income countries: how much, from where and to whom
Bibliographic record
Abstract
BACKGROUND: The need for sufficient and reliable funding to support health policy and systems research (HPSR) in low- and middle-income countries (LMICs) has been widely recognised. Currently, most resources to support such activities come from traditional development assistance for health (DAH) donors; however, few studies have examined the levels, trends, sources and national recipients of such support - a gap this research seeks to address. METHOD: Using OECD's Creditor Reporting System database, we classified donor funding commitments using a keyword analysis of the project-level descriptions of donor supported projects to estimate total funding available for HPSR-related activities annually from bilateral and multilateral donors, as well as the Bill and Melinda Gates Foundation, to LMICs over the period 2000-2014. RESULTS: Total commitments to HPSR-related activities have greatly increased since 2000, peaked in 2010, and have held steady since 2011. Over the entire study period (2000-2014), donors committed a total of $4 billion in funding for HPSR-related activities or an average of $266 million a year. Over the last 5 years (2010-2014), donors committed an average of $434 million a year to HPSR-related activities. Funding for HPSR is heavily concentrated, with more than 93% coming from just 10 donors and only represents approximately 2% of all donor funding for health and population projects. Countries in the sub-Saharan African region are the major recipients of HPSR funding. CONCLUSION: Funding for HPSR-related activities has generally increased over the study period; however, donor support to such activities represents only a small proportion of total DAH and has not grown in recent years. Donors should consider increasing the proportion of funds they allocate to support HPSR activities in order to further build the evidence base on how to build stronger health systems.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.073 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".