Analysis of the policymaking process in Burkina Faso’s health sector: case studies of the creation of two health system support units
Bibliographic record
Abstract
BACKGROUND: Burkina Faso has made a number of health system policy decisions to improve performance on health indicators and strengthen responsiveness to health-related challenges. These included the creation of a General Directorate of Health Information and Statistics (DGISS) and a technical unit to coordinate performance-based financing (CT-FBR). We analysed the policymaking processes associated with the establishment of these units, and documented the factors that influenced this process. METHOD: We used a multiple-case study design based on Kingdon's agenda-setting model to investigate the DGISS and CT-FBR policymaking processes. Data were collected from interviews with key informants (n = 28), published literature, policy documents (including two strategic and 230 action plans), and 55 legal/regulatory texts. Interviews were analysed using thematic qualitative analysis. Data from the documentary analysis were triangulated with the qualitative interview data. RESULTS: Key factors influencing the policymaking processes associated with the two units involved the 'problem' (problem identification), 'policy' (formation of policy proposals), and 'politics' (political climate/change) streams, which came together in a way that resulted in proposals being placed on the decision agenda. A number of problems with Burkina Faso's health information and financing systems were identified. Policy proposals for the DGISS and CT-FBR units were developed in response to these problems, emerging from several sources including development partners. Changes in political and public service administrations (specifically the 2008 appointment of a new Minister of Health and the establishment of a new budget allocation system), with corresponding changes in the actors and interests involved, appeared key in elevating the proposals to the decision agenda. CONCLUSIONS: Efforts to improve performance on health indicators and strengthen responsiveness to health-related challenges need focus on the need for a compelling problem, a viable policy, and conducive politics in order to make it to the decision agenda.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".