Brokers, Gatekeepers, and Power Centers: A Social Network Analysis of Nigeria’s Universal Health Coverage Policy Landscape
Bibliographic record
Abstract
This study examines Nigeria’s slow progress toward universal healthcare coverage (UHC), analyzing the policy development process and the roles of various actors involved. Using social network analysis (SNA) and qualitative methods, we conducted structured interviews and documentary analysis to identify key policy actors and their relationships within the UHC network. The SNA revealed a complex structural network categorizing actors into four groups: power actors, peripheral actors, gatekeepers (brokers), and isolated actors. These classifications determined their access to information, professional support, and resources. A stable subset of seven organizations demonstrated significant centrality and influence across multiple relationships, while other organizations were central only in specific contexts. Senior government officials emerged as the primary influencers in UHC policy development and decision-making, controlling information flow within the policy network. These actors played crucial roles in advancing and impeding UHC progress in Nigeria. The study identifies political, policy-oriented, financial, and organizational constraints as significant barriers to UHC implementation. Addressing these challenges is essential for improving financial protection and ensuring Nigerians have equitable access to essential healthcare services. Our findings contribute to understanding the complex interplay of actors in healthcare policy development in low- and middle-income countries, offering insights that could help accelerate progress toward achieving UHC in Nigeria and similar settings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".