Bibliographic record
Abstract
We thank Choubey et al1 for the letter regarding our article on women leadership in liver transplantation (LT).2 We are glad to hear that other institutions also have conducted analysis on this important topic alongside the International Liver Transplantation Society Equality, Diversity, and Inclusion Committee, aiming to gain further understanding on the gender gap within the field of transplantation. We agree that identifying the current gender composition of transplant programs is only the first step on recognizing the current paradigm. The causes of the gender leadership gap in LT are complex and mutifactorial, complicated by various cultural norms in different countries, institutions, and medical specialties. In addition to providing women physicians the access to leadership positions, advocacy for women in LT requires the elimination of the gender gap in professional promotion and compensation.3 Providing women physicians the access to leadership positions is not simply having men put women in leadership positions as “tokens” to meet the diversity quota. To assist with creating gender neutrality in leadership roles, medical institutions should remove present barriers as different perceived higher standard of performance from women physicians and preference for men due to gender similarity in promotion decisions. Removing gender specificity on the labels of leadership roles, such as changing the title from “Chairman” to a gender neutral “Chair” or “Chairperson,” can be a basic start. For academic promotion, data have shown disparities in the experiences of men and women. Women often must produce far more than men to achieve promotion.4 A clear promotional criteria should be established and consistently utilized to rate academic performance equally for both genders. Women physicians should also seek clarity on which scholar activities have higher impacts in the promotional process, focusing their time and effort on those tasks with higher promotional values. Recent studies continue to find compensation differences between men and women physicians, even after adjusting for multiple factors including, but not limited to, age, experience, work hours, years in practice, productivity, and academic rank.5 The gender pay gap remained and widened over the course of a woman physician’s career.3,5 Equitable physician compensation based on comparable work with transparency and routine assessment of the equity on pay may mitigate the gender pay gap.5 Although the field of medicine continues its efforts on improving equality and equity, women physicians should also actively seek educational programs in negotiation, training in career and leadership development, mentorships and sponsorships, and opportunities in networking. Gender equality and equity can only be achieved if women physicians continue to advocate for themselves and be the change. We hope our article brings awareness to the gender leadership gap and further strengthens the movement of gender equity and equality in the field of LT.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.037 | 0.043 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".