Virtual medical research mentoring and collaboration: breaking the bounds of nationality during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. OnBehalf Cardioovascular Analytics Group Background Medical research is critical to professional advancement, and mentoring is an important means of early research engagement in medical training. In contrast to international research collaborations, research mentoring programs are often locally limited. With the COVID-19 pandemic causing drifts to virtual classes and conferences, virtual international medical research mentoring may be viable. We hereby describe our experience with a virtual, international mentorship group for cardiovascular research. Methods Our virtual international research mentorship group has been running since 2015. The group focuses on risk stratification and outcomes research in cardiovascular medicine and epidemiology. Mentees from any country or region in all stages of medical careers are welcomed. Considering the increasing emphasis of contemporary research on multidisciplinary healthcare and translational research, our team also includes allied healthcare professionals or students, and graduates from natural sciences (Figure 1). With our members’ diverse backgrounds, we firmly adhere to the principle that all members must be given equal opportunities and treatment, regardless of their age, gender, race, nationality, sexual orientation, family background, and institution of study or practice. We make use of virtual platforms and multi-level mentoring (both senior and peer mentoring), and emphasize active participation, early leadership, open culture, accessible research support, and a distributed research workflow (i.e. an accessible-distributed model). Results Since establishment, our group has expanded to include 63 active members from 14 countries (Figure 2), leading a total of 109 peer-reviewed original studies and reviews published. We observed no significant difficulty in communication between team members, nor conflicts due to differences in nationality or ethnicity. Most studies involve cross-country and ethnicity collaborations, and inter-disciplinary and inter-regional knowledge exchanges are frequent. Multi-level mentoring ensured mentoring quality without compromising bonding and communication. Conclusion An accessible-distributed model of virtual international medical research collaboration and multi-level mentoring is viable, efficient, and caters to the needs of contemporary healthcare. We hope that others will build similar models and improve medical research mentoring globally. Abstract Figure 1 Abstract Figure 2
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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 | 0.049 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.036 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".