Scaling-Up Performance-Based Financing in Burkina Faso: From PBF to User Fees Exemption Strategic Purchasing
Bibliographic record
Abstract
BACKGROUND: Numerous countries have undertaken performance-based financing (PBF) reforms to improve quality and quantity of healthcare services. However, only few reforms have successfully managed to achieve the different scale-up phases. In Burkina Faso, a pilot project was implemented, but was put on hold before being scaled. During the writing of this article, discussions to scale-up were still ongoing on a national strategic purchasing strategy within a government led user fee exemption policy. METHODS: This study's objective is to identify facilitators and barriers to scaling-up for that pilot, based on the World Health Organization's (WHO's) theoretical framework. Interviews were conducted in three health centres and in Ouagadougou to discuss the scale-up with different actors. The software QDA Miner© was used to help in the framework analysis. RESULTS: The low involvement of some key stakeholders (mainly decision-makers) and the unstable context hindered ownership of the project, thus its priority on the political agenda. PBF reform therefore lost its momentum to the benefit of a user fee exemption policy. This latter program was seen to be more beneficial since it addressed access to healthcare services, in comparison to service quality, which was the PBF's relative advantage. A scale-up of some PBF elements (eg, strategic purchasing tools) is however still in discussion in 2019, but would be integrated within the user fee exemption program. Increased costs during the PBF's implementation gave the impression that the project was too costly and not scalable. The involvement of an important funding agency (World Bank, WB) also fed the impression of high costs, which demotivated the actors, especially decision-makers. CONCLUSION: Contextual factors remain central to the implementation of PBF, while their evaluation and mitigation have remained unclear. The participation of key actors in scaling-up operations and the use of social science as tools to better understand the context is therefore primordial.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".