Peer-learning and support among health policy and systems research actors in West Africa: a social network analysis
Bibliographic record
Abstract
BACKGROUND: Health policy and systems research (HPSR) is vital for strengthening health systems, yet its development in West Africa remains constrained by limited capacity. To strengthen capacity, the West African Network of Emerging Leaders in Health Policy and Systems (WANEL) was created to foster peer learning and cross-country collaboration among early- and mid-career HPSR actors. This study used Social Network Analysis (SNA) to examine WANEL's structure and functioning, with the aim of understanding how well the network supports its capacity and HPSR field-building goals. METHODS: A cross-sectional whole-network survey was conducted with all 103 WANEL members, supported by document reviews and qualitative interviews. Relationship types assessed included acquaintance, communication, advice, mentorship and research collaboration. Data were analysed using Gephi to visualize relational patterns and compute metrics such as density and centralization, while qualitative findings provided context for interpreting network dynamics. RESULTS: While WANEL has enhanced cross-country awareness and disciplinary diversity, the network exhibits low cohesion and high centralization. Key support relationships, particularly mentorship, advice and collaboration are sparse and unevenly distributed. A few actors dominate the flow of information and access to opportunities, while many, especially early-career and francophone actors, remain peripheral or isolated. Network interactions are driven by prior relationships and linguistic or professional affinity, limiting broader engagement. CONCLUSION: Findings reveal structural barriers that constrain WANEL's potential to act as an inclusive platform for HPSR capacity-strengthening. To fulfil its vision, the network must address its current fragmentation by building stronger cross-cutting ties, broadening participation and decentralizing influence. This study contributes empirical insights into the design and governance of regional HPSR networks in low- and middle-income contexts and underscores the importance of relational infrastructure in advancing collective capacity.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".