Analysis of funding landscape for health policy and systems research in the Eastern Mediterranean Region: A scoping review of the literature over the past decade
Bibliographic record
Abstract
BACKGROUND: Health policy and systems research (HPSR) can strengthen health systems and improve population health outcomes. In the Eastern Mediterranean Region (EMR), there is limited recognition of the importance of HPSR and funding remains the main challenge. This study seeks to: (1) assess the reporting of funding in HPSR papers published between 2010 and 2022 in the EMR, (2) examine the source of funding in the published HPSR papers in the EMR and (3) explore variables influencing funding sources, including any difference in funding sources for coronavirus disease 2019 (COVID-19)-related articles. METHODS: We conducted a rapid scoping review of HPSR papers published between 2010 and 2022 (inclusively) in the EMR, addressing the following areas: reporting of funding in HPSR papers, source of funding in the published HPSR papers, authors' affiliations and country of focus. We followed the Joanna Briggs Institute (JBI) guidelines for conducting scoping reviews. We also conducted univariate and bivariate analyses for all variables at 0.05 significance level. RESULTS: Of 10,797 articles screened, 3408 were included (of which 9.3% were COVID-19-related). More than half of the included articles originated from three EMR countries: Iran (n = 1018, 29.9%), the Kingdom of Saudi Arabia (n = 595, 17.5%) and Pakistan (n = 360, 10.6%). Approximately 30% of the included articles did not report any details on study funding. Among articles that reported funding (n = 1346, 39.5%), analysis of funding sources across all country income groups revealed that the most prominent source was national (55.4%), followed by international (41.7%) and lastly regional sources (3%). Among the national funding sources, universities accounted for 76.8%, while governments accounted for 14.9%. Further analysis of funding sources by country income group showed that, in low-income and lower-middle-income countries, all or the majority of funding came from international sources, while in high-income and upper-middle-income countries, national funding sources, mainly universities, were the primary sources of funding. The majority of funded articles' first authors were affiliated with academia/university, while a minority were affiliated with government, healthcare organizations or intergovernmental organizations. We identified the following characteristics to be significantly associated with the funding source: country income level, the focus of HPSR articles (within the EMR only, or extending beyond the EMR as part of international research consortia), and the first author's affiliation. Similar funding patterns were observed for COVID-19-related HPSR articles, with national funding sources (78.95%), mainly universities, comprising the main source of funding. In contrast, international funding sources decreased to 15.8%. CONCLUSION: This is the first study to address the reporting of funding and funding sources in published HPSR articles in the EMR. Approximately 30% of HPSR articles did not report on the funding source. Study findings revealed heavy reliance on universities and international funding sources with minimal role of national governments and regional entities in funding HPSR articles in the EMR. We provide implications for policy and practice to enhance the profile of HPSR in the region.
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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 | 0.134 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.045 | 0.045 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".