Financing healthcare in Gulf Cooperation Council countries: a focus on Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: This paper presents an analysis of the main characteristics of the Gulf Cooperation Council's (GCC) health financing systems and draws similarities and differences between GCC countries and other high-income and low-income countries, in order to provide recommendations for healthcare policy makers. The paper also illustrates some financial implications of the recent implementation of the Compulsory Employment-based Health Insurance (CEBHI) system in Saudi Arabia. METHODS: Employing a descriptive framework for the country-level analysis of healthcare financing arrangements, we compared expenditure data on healthcare from GCC and other developing and developed countries, mostly using secondary data from the World Health Organization health expenditure database. The analysis was supported by a review of related literature. RESULTS: There are three significant characteristics affecting healthcare financing in GCC countries: (i) large expatriate populations relative to the national population, which leads GCC countries to use different strategies to control expatriate healthcare expenditure; (ii) substantial government revenue, with correspondingly high government expenditure on healthcare services in GCC countries; and (iii) underdeveloped healthcare systems, with some GCC countries' healthcare indicators falling below those of upper-middle-income countries. CONCLUSION: Reforming the mode of health financing is vital to achieving equitable and efficient healthcare services. Such reform could assist GCC countries in improving their healthcare indicators and bring about a reduction in out-of-pocket payments for healthcare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".