Cardiovascular Research Mentorship Platforms
Bibliographic record
Abstract
Background: There has been increasing awareness on the issue of underrepresentation in academic cardiology. However, to date, most mentorship programs are not designed specifically tailored for future careers in cardiology or cardiovascular medicine. We present our 6-year experience in running two research mentorship platforms, the International Health Informatics Study Network and the Cardiovascular Analytics Group. Objective: To study the underrepresentation in academic cardiology. Methods: Researchers were prospectively recruited into the mentorship programs between September 2015 and September 2021. A combination of online mentorship approaches was employed, including one-to-one mentoring (between faculty and students and between peers), group mentorship, and teaching sessions. Outcomes included the number of publications related to cardiovascular medicine, including those with student members in key authorship positions, and students serving as mentors. Female representation was assessed. Results: A total of 117 researchers from 19 countries were recruited between September 2015 and September 2021, leading to the successful publication of 164 research articles on cardiovascular medicine or epidemiology. Students participated in 80% of the articles ( n = 131). At least one student served as the first author in 34% of the articles ( n = 56; at least one female student as the first author in 48% of the 56 articles; n = 27), as the senior author in 7.3% of the articles ( n = 12), and as a mentor in 15% of the articles ( n = 26; at least one female student served as a mentor in 42% of the 26 articles; n = 11). Female researchers occupied one of the four key authorship positions in 43% of the articles ( n = 70; 47 female first authors; 10 female co-first authors; 6 female co-corresponding authors; and 17 female last authors). There was a 12% increase in the percentage of females in key authorship positions between the periods 2016–2018 and 2019–2021, from 47% ( n = 33) and 53% ( n = 37) of the 70 publications having at least one female in key authorship positions, respectively. Conclusions: Online-based mentorship programs can promote the development of independent research and leadership skills in students, with a positive impact on diversity, gender equity, inclusion, and productivity in cardiovascular research.
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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 | 0.059 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.011 |
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".