Transnational Networks’ Contribution to Health Policy Diffusion: A Mixed Method Study of the PerformanceBased Financing Community of Practice in Africa
Bibliographic record
Abstract
BACKGROUND: Transnational networks such as Communities of Practice (CoPs) are flourishing, yet their role in diffusing health systems reforms has been seldom investigated. Over the past decade, performance-based financing (PBF) has rapidly spread in Africa. This study explores how, through the PBF Community of Practice's attributes, structure, and strategies, PBF diffusion was fostered in sub-Saharan Africa (SSA). METHODS: Informed by the diffusion entrepreneurs' (DEs) framework dimensions, we used a mixed methods convergent design to investigate how the attributes, structure, and strategies of this community fostered the diffusion of PBF. The quantitative strand of work included firstly a semantic discourse analysis of textual data extracted from CoP's online discussion forum (n=1346 posts). Secondly, the relational data extracted from these 1346 forum posts was examined using social network analysis (SNA). We confronted these quantitative results with a thematic analysis of qualitative interviews (n=40) and data extracted from the CoP's key documentation (n=17). RESULTS: CoP members' attributes included: representation systems anchored in clinical and economic sciences, strong expectations that the CoP would boost professional visibility and career, and significant health systems knowledge and social resources. The CoP's core group, dominated by high-income country (HIC) members, critically matched PBF principles to major health systems issues in Africa. The broad consensus in online PBF thematic discussions created a strong sense of community, a breeding ground for emulation among CoP members. The CoP also sought to produce and promote experiential knowledge exchanges about PBF amongst African practitioners. Findings from network analyses showed that the promoted Africa-driven community was led by HIC members, although their prominence tended to decrease with time. CONCLUSION: This empirical research highlighted some of the constituting features, structure, and strategies of policy networks in influencing health policy diffusion. Despite good intentions to disrupt the established governance landscape, influential actors coming from HICs continued to drive the framing, and shaped health systems policy experimentation, emulation, and learning in African countries. Beyond mere knowledge exchange platforms, CoP can act as meaningful transnational policy networks pursuing the diffusion of health systems reforms, such as PBF.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| 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".