What Makes a Great Mentor: Interviews With Recipients of the ATVB Mentor of Women Award
Bibliographic record
Abstract
he American Heart Association Council on Arteriosclerosis, Thrombosis and Vascular Biology (ATVB) brings together a unique group of worldleading scientists from diverse fields within the ATVB research communities.The ATVB Council's Women's Leadership Committee aims to promote excellence among women in science and academic medicine and raise new leaders.Our goals are to encourage the advancement of women's scientific careers, promote visibility, and provide forums for professional networking to cultivate collaboration within and outside the ATVB community.Every year, the Women's Leadership Committee honors established scientists of the ATVB Council whose actions have demonstrated their exceptional service in the mentorship, support, advocacy, and sponsorship of women in the field of cardiovascular biology with the ATVB Mentor of Women Award.This year, as the Women's Leadership Committee celebrates its 20th anniversary, the members of this committee interviewed 11 past recipients of the Mentor of Women Awards: Mary Sorci-Thomas (2002), Alan Daugherty (2010), Rama Natarajan (2011), Lisa Cassis (2012), Coleen McNamara (2014), Lynn Hedrick (2015), Muredach Reilly (2016), Kerry-Anne Rye (2017), Kathryn Moore (2018), Nancy Webb (2019), and Murray Huff (2020; Figure 1).Our mentors were asked to provide answers to 11 questions that reflect their vision of outstanding mentorship.The Women's Leadership Committee is thrilled to report the insights gained from these interviews and hopes it will serve as a useful reference to both mentors and mentees.
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".