Beyond fragmentation and towards universal coverage: insights from Ghana, South Africa and the United Republic of Tanzania
Bibliographic record
Abstract
The World Health Assembly of 2005 called for all health systems to move towards universal coverage, defined as " access to adequate health care for all at an affordable price" . A crucial aspect in achieving universal coverage is the extent to which there are income and risk cross-subsidies in health systems. Yet this aspect appears to be ignored in many of the policy prescriptions directed at low- and middle-income countries, often resulting in high degrees of health system fragmentation. The aim of this paper is to explore the extent of fragmentation within the health systems of three African countries (Ghana, South Africa and the United Republic of Tanzania). Using a framework for analysing health-care financing in terms of its key functions, we describe how fragmentation has developed, how each country has attempted to address the arising equity challenges and what remains to be done to promote universal coverage. The analysis suggests that South Africa has made the least progress in addressing fragmentation, while Ghana appears to be pursuing a universal coverage policy in a more coherent way. To achieve universal coverage, health systems must reduce their reliance on out-of-pocket payments, maximize the size of risk pools, and resource allocation mechanisms must be put in place to either equalize risks between individual insurance schemes or equitably allocate general tax (and donor) funds. Ultimately, there needs to be greater integration of financing mechanisms to promote universal cover with strong income and risk cross-subsidies in the overall health system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".