2023 Update on equity, diversity, and inclusion in Canadian cardiac surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite efforts to diversify the medical field, cardiac surgery remains amongst the least diverse specialties. Specifically, the percentage of women and racial minorities has remained low in past few decades. This may impact prospective trainee recruitment and surgical care. This paper highlights recent efforts that aim to promote diversity and inclusion of the Canadian cardiac surgical workforce. RECENT FINDINGS: Formal programs have been established to support students at different stages of training. In 2022, the Canadian Society for Cardiac Surgery has released an equity, diversity, and inclusion statement to summarize the current state and the strategic goals to accomplish a more just working environment. At the local level, the University of Toronto Next Surgeon high school pilot program, provided low-income, women, and racial minority students mentorship and experiential exposure to our field. Also, the University of Toronto, scholarships funded summer research with cardiac surgeons for women, as well as Black and Indigenous medical students. SUMMARY: Tangible efforts that target high school, undergraduate, and medical students are underway to promote equity and diversity of cardiac surgeons in Canada. Future studies that evaluate the gaps and identify bottlenecks could better guide interventions at institutions across the country.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".