Why did performance-based financing in Burkina Faso fail to achieve the intended equity effects? A process tracing study
Bibliographic record
Abstract
In recent years, performance-based financing (PBF) has attracted attention as a means of reforming provider payment mechanisms in low- and middle-income countries. Particularly in combination with demand-side interventions, PBF has been assumed to benefit also the most vulnerable and disadvantaged groups. However, impact evaluations have often found this not to be the case. In Burkina Faso, PBF was coupled with specific equity measures to enhance healthcare utilization among the ultra-poor, but failed to produce the expected effects. Our study used the process tracing methodology to unravel the reasons for the lack of impact produced by the equity measures. We relied on published evidence, secondary data analysis, and findings from a qualitative study to support or invalidate the hypothesized causal mechanism, that is the reconstructed theory of change of the equity measures. Our findings show how various contextual, design, and implementation challenges hindered the causal mechanism from unfolding as planned. These included issues with the identification and exemption of the ultra-poor on the demand side, and with financial issues and considerations on the supply side. In broader terms, our findings underline the difficulty in improving access to care for the ultra-poor, given the multifaceted and complex nature of barriers to care the most vulnerable face. From a methodological point of view, our study demonstrates the value and applicability of process tracing in complementing other forms of evaluation for complex interventions in global health.
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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 | 0.064 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".