Progress towards universal coverage: the health systems of Ghana, South Africa and Tanzania
Bibliographic record
Abstract
A desire to enhance protection against health care costs and improve equity of access to health care lies at the core of many health sector financing initiatives. Until recently, international debates about financing and health equity have focused primarily on mechanisms to promote equity in relation to very specific elements of health systems. However, in recent years there has been growing interest in considering these equity challenges from a more systemic perspective. In this context, universal health coverage is becoming a rallying call, with a focus on how best universal coverage can be financed. This paper is the first in a special issue which presents a body of research whose overall aim was to critically evaluate existing inequities in health care financing and provision in Ghana, South Africa and Tanzania, and the extent to which health insurance mechanisms (broadly defined) could address financial protection and equity of access challenges. In this first paper we introduce the countries' health systems, with a special emphasis on existing mechanisms for financial protection. We also identify in broad terms the key challenges for universal coverage, setting the scene for the subsequent papers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".