Using Social Network Analysis to Inform Implementation Science Infrastructure Development
Bibliographic record
Abstract
Implementation is an inherently collaborative and transdisciplinary activity; however, engaging key partners across research, practice, and policy sectors is challenging. Successful implementation requires supportive infrastructure for both research and practice. This paper presents practice-based reflections on the value of exploratory social network analysis during the early phases of developing implementation infrastructure in Alberta, Canada. Specifically, we argue that exploratory social network analysis, when paired with follow-up qualitative interviews, can help identify local implementation science assets, inform network-building, and promote implementation support services to target users. Exploratory social network analysis helped our team identify key implementation researchers and implementation support practitioners in Alberta's health-research ecosystem. The analysis also showed that implementation research in the province of Alberta follows a consultation model, with one-way assistance requests, while implementation practice is more collaborative in nature. The follow-up interviews provided an opportunity to engage with teams across the networks and allowed participants to contextualize the social network analysis findings. This uncovered: (1) widespread need for implementation science capacity-building, and (2) key implementation partnership considerations. These results illustrate how organizations can employ social network analysis in practical ways to inform implementation infrastructure development. Supplementary Information: The online version contains supplementary material available at 10.1007/s43477-025-00180-8.
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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 | 0.052 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".