How is the discourse of performance-based financing shaped at the global level? A poststructural analysis
Bibliographic record
Abstract
BACKGROUND: Performance-based financing (PBF) in low- and middle-income settings has diffused at an unusually rapid pace. While many studies have looked at PBF implementation processes and effects, there is an empirical research gap investigating the ways PBF has diffused. Discursive processes are paramount elements of policy diffusion because they explain the origins of essential elements of the political debate on PBF. Using Bacchi's poststructural approach that emphasises problem representations embedded in the discourse, the present study analyses the construction of the global discourse on PBF. METHODS: A rich corpus of qualitative data (57 in-depth interviews and 10 observation notes) was collected. The transcribed material was coded using QDAMiner©. Codes were assembled to populate analytical categories informed by the framework on diffusion entrepeneurs and Bacchi's poststructural approach. RESULTS: Our results feature problem representations shaped and spread by PBF global diffusion entrepreneurs. We explain how these representations reflected diffusion entrepreneurs' own belief systems and interests, and conflicted with those of non-diffusion entrepreneurs. This research also reveals the specific strategies global diffusion entrepreneurs engaged in to effectively diffuse PBF, through reflecting problem representations based on the discourse on PBF, and inducing certain forms of policy experimentation, emulation, and learning. CONCLUSIONS: Bacchi's poststructural approach is useful to analyse the construction of global health problem representations and the strategies set by global diffusion entrepreneurs to spread these representations. Future research is needed to investigate the belief systems, motivations, resources, and strategies of actors that shape the construction of global health discourses.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".