Using network science to understand the knowledge exchange pathways in health systems research
Bibliographic record
Abstract
Evidence-informed public policy has demonstrated positive outcomes for populations. Within the health sector, the concept of evidence-informed decision-making (EIDM) suggests that knowledge generated from scientific research will be translated into evidence to support better policy. To facilitate this process, the concept of knowledge translation (KT) was developed within the Canadian health context fewer than 20 years ago. Achieving the aspirational goals of EIDM and KT has proven difficult. Literature reviews have found that only 20% of knowledge transfer and exchange studies discussed real-world application and 14% of health research findings enter day-to-day practice, taking 17-20 years to do so. New knowledge emerges through collaboration. Many aspects of KT involve complex social processes fundamentally embedded in relationships. There is compelling research showing a group’s success in solving complex problems is primarily correlated with the quality of relationships individuals form. Existing frameworks are almost devoid of interpersonal knowledge exchange (KE) networks and therefore only tell part of the story. Epidemiology has embraced network science for quantitatively describing the transmission dynamics of communicable diseases as a contagion phenomenon. This dissertation uses a similar approach to suggest that KE shares fundamental properties with other contagions. The characteristics of individuals as well as the underlying network structure and heterogeneous patterns of combining and exchanging knowledge translates seamlessly. Two applications are used to support this novel contribution. At the macro- level, a bibliometric analysis is used to understand the international co-authorship trends in health policy and systems research (HPSR). The resulting data were used in a network analysis to understand the degree to which economic regions served by HPSR actually participate. At the micro-level, a survey was conducted in a public health agency with an embedded research mandate. The survey captured demographics, knowledge about research and interpersonal networks on which research knowledge flows. These results were used to show the knowledge exchange pathways within the respective networks. Bibliometric and survey outcomes parameterize scalable, generalizable networks. Both macro- and micro- applications use networks to develop strategies and highlight metrics that facilitate meaningful inclusion of the intended end users throughout the research process to improve KE for EIDM.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".