Removing user fees in the health sector: a review of policy processes in six sub-Saharan African countries
Bibliographic record
Abstract
In recent years, governments of several low-income countries have taken decisive action by removing fully or partially user fees in the health sector. In this study, we review recent reforms in six sub-Saharan African countries: Burkina Faso, Burundi, Ghana, Liberia, Senegal and Uganda. The review describes the processes and strategies through which user fee removal reforms have been implemented and tries to assess them by referring to a good practice hypotheses framework. The analysis shows that African leaders are willing to take strong action to remove financial barriers met by vulnerable groups, especially pregnant women and children. However, due to a lack of consultation and the often unexpected timing of the decision taken by the political authorities, there was insufficient preparation for user fee removal in several countries. This lack of preparation resulted in poor design of the reform and weaknesses in the processes of policy formulation and implementation. Our assessment is that there is now a window of opportunity in many African countries for policy action to address barriers to accessing health care. Mobilizing sufficient financial resources and obtaining long-term commitment are obviously crucial requirements, but design details, the formulation process and implementation plan also need careful thought. We contend that national policy-makers and international agencies could better collaborate in this respect.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".