Promoting Female Leadership in Academic Surgery: Disrupting Systemic Gender Bias
Bibliographic record
Abstract
Gender bias is a pervasive issue in academic surgery and is characterized by familiar patterns previously described in the business world. In this article, the authors illuminate gender bias patterns in academic surgery identified in prior in-depth interviews with female surgical department chairs across the United States. The 4 main gender bias patterns drawn from the business world and illuminated with data from the interviews are (1) prove-it-again, (2) tightrope or double-blind dilemma, (3) maternity wall or benevolent bias, and (4) tug-of-war. The authors propose steps to disrupt systemic gender bias issues recognized in the academic surgery community. The proposed steps are informed by guidance from surgical diversity task forces, by existing literature, and by the authors' own experiences in the field. The steps are divided into 3 main categories: education, structured mentorship, and transparency. The proposed changes include improving training and recognition of unconscious bias, establishing level-appropriate and deliberate mentorship across all stages of training and practice, standardizing promotional requirements, and eliminating outdated standards that contribute to the gender pay gap. Although this article addresses gender bias in academic surgery, the proposed steps toward change can promote equity across the surgical community as a whole and extend to other underrepresented groups in the field.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".