Learning from health system reforms: lessons from Burkina Faso
Bibliographic record
Abstract
OBJECTIVES: Burkina Faso has implemented a macroeconomic adjustment programme (MAP) along with an ambitious reform of the health care system. Our aim was (1) to verify whether MAPs led to a reduction in health resources, and (2) to analyze the consequences of health policies implemented. METHOD: Cross-sectional and retrospective study, spanning the years 1983-2003. The macro aspect is based upon documents from national and international sources, a database of secondary socioeconomic data, and interviews of key informants working in upper management. Household and health facility surveys were conducted in three regions covering 53 communities. RESULTS: Within the reforms, the health sector benefited from an important flow of resources. There were significant increases in public expenditures, health care staff, the number of primary care facilities and the availability of generic drugs. However, health facilities in the public sector remain underused and major inequities subsist. Access to health care is constrained by the population's ability to pay. Health expenditures impoverish households, creating new poor and impoverishing the already poor. CONCLUSIONS: The success of reforms depends largely on the extent to which they remove financial barriers to access to services. The experience of Burkina Faso also reveals the need for fundamental changes that will motivate staff, improve productivity, and ensure good quality services. Integrating health development policies with strategic plans for poverty reduction can provide new opportunities for African countries to redesign their health systems within this type of perspective.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".