Describing the state of a research network: A mixed methods approach to network evaluation
Bibliographic record
Abstract
Diabetes Action Canada Strategy for Patient-Oriented Research (SPOR) Network in Chronic Disease was formed in 2016 and is funded primarily through the Canadian Institutes of Health Research (CIHR). We propose a novel mixed-methods approach to a network evaluation integrating the State of Network Evaluation framework and the Canadian Academy of Health Sciences (CAHS) preferred framework and indicators. We measure key network themes of connectivity, health and results, and impact and return on investment associated with health research networks. Our methods consist of a longitudinal cross-sectional network survey of members and social network analysis to examine Network Connectivity and assess the frequency of interactions, the topics discussed during them, and how networking effectively facilitates interactions and collaboration among members. Network Health will be evaluated through semistructured interviews, a membership survey inquiring about satisfaction and experience with the Network, and a review of documentary sources related to funding and infrastructure to evaluate Network Sustainability. Finally, we will examine Network Results and Impact using the CAHS preferred framework and indicators to measure returns on investment in health research across the five domains of the CAHS framework, which include: advancing knowledge, capacity building, informing decision making, health impact, and economic and social impact. Indicators will be assessed with various methods, including bibliometric analyses, review of relevant documentary sources (annual reports), member activities informing health and research policy, and Patient Partner involvement. The Network Evaluation will provide members and stakeholders with information for planning, improvements, and funding future Network endeavors.
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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 | 0.463 | 0.483 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".