Performance-based financing in low-income and middle-income countries: isn’t it time for a rethink?
Bibliographic record
Abstract
This paper questions the view that performance-based financing (PBF) in the health sector is an effective, efficient and equitable approach to improving the performance of health systems in low-income and middle-income countries (LMICs). PBF was conceived as an open approach adapted to specific country needs, having the potential to foster system-wide reforms. However, as with many strategies and tools, there is a gap between what was planned and what is actually implemented. This paper argues that PBF as it is currently implemented in many contexts does not satisfy the promises. First, since the start of PBF implementation in LMICs, concerns have been raised on the basis of empirical evidence from different settings and disciplines that indicated the risks, cost and perverse effects. However, PBF implementation was rushed despite insufficient evidence of its effectiveness. Second, there is a lack of domestic ownership of PBF. Considering the amounts of time and money it now absorbs, and the lack of evidence of effectiveness and efficiency, PBF can be characterised as a donor fad. Third, by presenting itself as a comprehensive approach that makes it possible to address all aspects of the health system in any context, PBF monopolises attention and focuses policy dialogue on the short-term results of PBF programmes while diverting attention and resources from broader processes of change and necessary reforms. Too little care is given to system-wide and long-term effects, so that PBF can actually damage health services and systems. This paper ends by proposing entry points for alternative approaches.
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 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 | 0.037 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".