Bibliographic record
Abstract
The geography of healthcare financing is important in addressing inequities and inequalities in population health. This is particularly true in developing countries where there are significant disparities in socioeconomic and health status between regions. Many countries, however, are adopting a fiscal federal system, in which decision-making about the use of state financial resources is granted to lower levels of government. What impact does this have on the equitable distribution of resources to primary health care? Decentralisation has the potential to improve the efficiency of health service delivery and speed up the response to community needs, but are sufficient funds being allocated to where they are most needed? Primary Healthcare Spending highlights key factors that can help to achieve equity in the allocation of primary healthcare resources within fiscal federal systems and decentralised health systems in general. It explores a wide range of ways of spending found in fiscal federal systems around the world and how they impact on the equitable distribution of primary healthcare resources. Although South Africa is used as a case for discussion, the issues raised in the book are relevant to all countries operating under a fiscal federal system and those that operate a decentralised health system. Primary Healthcare Spending is an important reference for policymakers in health organisations, researchers in the field of health policy and health economics, agencies involved in providing support to health systems and students in the area of health administration and health policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.110 | 0.008 |
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; both teacher heads agree on what is shown here.
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".