Women Surgical Trainees Interested in Cardiothoracic Surgery Lack Woman Cardiothoracic Mentorship
Bibliographic record
Abstract
Introduction: Mentorship is a vital component of success for surgical trainees. As the field of cardiothoracic (CT) operation remains heavily male-dominated, the current state of same-gender mentorship for women trainees is unknown. Our aim was to better understand the needs of women trainees interested in CT operation. We hypothesized that women trainees would lack same-gender mentors affecting their interests, career goals and concerns. Methods: Responses from the pre- and post-event surveys from the Women in Thoracic Surgery January and June 2021 speed mentoring events were analyzed. Survey questions included trainee career interests, goals, and concerns about pursuing a career in CT surgery. Trainee-related and mentor-related characteristics were assessed. Results: 142 women trainees completed the pre-event survey and 61 attended the events and completed the post-event survey. Most trainees were age 25-34 (102/142, 72.4%) and interested in cardiac operation (66/142, 46.5%). Mentors within CT surgery (110/142, 77.5%) were common, but only 82/142 (58.2%) had any woman mentor and even fewer had women CT mentors (53/142, 38.4%). On univariate analysis, trainees with a woman CT mentor were older (49/53, 92.4% vs 64/85, 75.3%, p=0.02), interested in sub-specialization (46/53, 86.8% vs 59/85, 69.4%, p=0.02) and wanted an academic career (39/53, 73.6% vs 42/85, 49.4%, p=0.005). Conclusion: Women CT surgery trainees have unique concerns and are lacking same-gender CT operation mentors. Greater efforts to create opportunities for women trainees to connect with women CT mentors will help provide appropriate resources for success, particularly those pursuing an academic
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".