Engaging medical students in cardiac surgery: a focus on equity, diversity, and inclusion
Bibliographic record
Abstract
PURPOSE OF REVIEW: The Coronavirus Disease 2019 pandemic prohibited Canadian medical students from in-person observerships. This may be particularly detrimental to under-represented groups that may consider surgical subspecialties. To address the unprecedented need for alternative surgical career exploration and diversity within the profession, The University of Toronto Cardiac Surgery Interest Group and Division of Cardiac Surgery collaborated on virtual experiential programming. RECENT FINDINGS: Medical students were invited to virtual (1) observerships of a cardiac bypass case, (2) mentorship sessions with surgeons, (3) resident teaching sessions, (4) multidisciplinary case-based Heart Team discussions to further their understanding of the scope of Cardiac surgery, and (5) a virtual coronary anastomosis training program. Additionally, a comprehensive virtual program was spearheaded to increase interest in Cardiac surgery among low-income Black high school students. SUMMARY: Trainee response to the virtual education, mentorship, and skill acquisition was positive. Trainees reported high levels of interest in the profession, particularly among females and under-represented minorities, supporting the principles of equity diversity, and inclusion in Cardiac surgery.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".