Factors influencing the burden of health care financing and the distribution of health care benefits in Ghana, Tanzania and South Africa
Bibliographic record
Abstract
In Ghana, Tanzania and South Africa, health care financing is progressive overall. However, out-of-pocket payments and health insurance for the informal sector are regressive. The distribution of health care benefits is generally pro-rich. This paper explores the factors influencing these distributions in the three countries. Qualitative data were collected through focus group discussions and in-depth interviews with insurance scheme members, the uninsured, health care providers and managers. Household surveys were also conducted in all countries. Flat-rate contributions contributed to the regressivity of informal sector voluntary schemes, either by design (in Tanzania) or due to difficulties in identifying household income levels (in Ghana). In all three countries, the regressivity of out-of-pocket payments is due to the incomplete enforcement of exemption and waiver policies, partial or no insurance cover among poorer segments of the population and limited understanding of entitlements among these groups. Generally, the pro-rich distribution of benefits is due to limited access to higher level facilities among poor and rural populations, who rely on public primary care facilities and private pharmacies. Barriers to accessing health care include medical and transport costs, exacerbated by the lack of comprehensive insurance coverage among poorer groups. Service availability problems, including frequent drug stock-outs, limited or no diagnostic equipment, unpredictable opening hours and insufficient skilled staff also limit service access. Poor staff attitudes and lack of confidence in the skills of health workers were found to be important barriers to access. Financing reforms should therefore not only consider how to generate funds for health care, but also explicitly address the full range of affordability, availability and acceptability barriers to access in order to achieve equitable financing and benefit incidence patterns.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".