The national subsidy for deliveries and emergency obstetric care in Burkina Faso
Bibliographic record
Abstract
INTRODUCTION: To reduce financial barriers to health care services presented by user fees, Burkina Faso adopted a policy to subsidize deliveries and emergency obstetric care for the period 2006-2015. Deliveries and caesarean sections are subsidized at 80%; women must pay the remainder. The worst-off are fully exempted. METHODS The aim of this article is to document this policy's entire process using a health policy analytical framework. Qualitative data are drawn from individual interviews (n = 113 persons) and focus groups conducted with 344 persons in central government, three rural districts and one urban district. Quantitative data are taken from the national health information system in eight districts. RESULTS The policy was initiated in all districts concurrently, before all the technical instruments were ready. The subsidy is paid by the national budget (US$60 million, including US$10 million for the worst-off). Information activities, implementation and evaluation support have been minimal because of insufficient funding. Health workers and lay people have not always had the same information, such that the policy has not been uniformly applied. Coping strategies have been noted among health workers and the population, but there has been no attempt to impede the policy's implementation. At the time of the study, fixed-rate reimbursement for delivery (output-based) and overestimation of input costs were financially advantageous to health workers (bonuses) and management committees (hoarding). Very few of the worst-off have been exempted from payment because selection processes and criteria have not yet been defined and most health workers are unaware of this possibility. The upward trend in assisted deliveries since 2004 continued after the policy's introduction. CONCLUSIONS This ambitious policy expresses a strong political commitment but has not been adequately supported by international partners. Despite relatively tight administrative controls, health workers have figured out how to take advantage of the system. Some of the policy's instruments should be reviewed and clarified to improve its effectiveness.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".