Evaluating research collaboration networks among venous thromboembolism researchers before and during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic changed how researchers interact and collaborate.Communication moved online to electronic communication and social media platforms.Simultaneously, researchers around the world rapidly studied venous thromboembolism (VTE) in patients with COVID-19. 1 More than 20 randomized controlled trials (RCTs) were initiated independently in different countries to evaluate anticoagulants in patients hospitalized with COVID-19, leading to duplication, then later sparking international collaboration.[2][3][4] Similarly, there was an enormous collective effort to rapidly develop COVID-19-related VTE guidelines.[5][6][7][8][9][10][11][12] The early COVID-19 pandemic was a unique time in history, and it is not known how VTE researchers collaborated.Social network analysis is a powerful methodology used to evaluate research collaborations through a rigorous description and analysis of connections among individuals.13 With in-depth understanding of how VTE researchers collaborated during the early pandemic, we can better understand and improve collaboration.After approval by the University of Calgary's research ethics board, we distributed an online survey to members of 2 VTE research networks, CanVECTOR (the Canadian Venous Thromboembolism Research Network; Canada; n = 59) and INVENT (the International Network of VENous Thromboembolism Clinical Research Networks; international; n = 389), between 16 June 2020 and 25 June 2020, and on Twitter (Qualtrics, Provo, UT).We collected demographic data, and data on how researchers interacted before and during the early COVID-19 pandemic to lead and collaborate on research.We conducted an egocentered network analysis, which maps the connections from the perspective of a single person, because we did not have a list of all VTE researchers in the world for a whole network analysis.14 To assess collaboration for leaders of research projects, we asked 2 questions: (1) for thrombosis-related COVID-19 research projects that you are leading or coleading, please list the researchers who you consulted with to get feedback on your study idea/protocol; and (2) for your 2 largest non-COVID-19 thrombosis research projects that you developed in the last 2 years, please list the researchers who you consulted with to get feedback on your study idea/protocol.To assess researchers' contributions to others' projects we asked: (3) please list the lead researcher(s) for any thrombosis-related COVID-19 research projects that you actively contributed to by giving feedback on a study idea/protocol; and (4) please list the lead researcher(s) for any non-COVID-19 thrombosis research project that you actively contributed to in the last 2 years by giving feedback on a study idea/protocol.We then searched publicly available directories to confirm the institution and country of each named researcher.We assessed the size of the 4 collaborative research networks for each respondent, and the percentage of their network
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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 | 0.075 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".