Gender and Racial Disparity Among Liver Transplantation Professionals: Report of a Global Survey
Bibliographic record
Abstract
Equality, diversity, and inclusion (EDI) are fundamental principles. Little is known about the pattern of practice and perceptions of EDI among liver transplant (LT) providers. International Liver Transplant Society (ILTS) EDI Committee survey around topics related to discrimination, mentorship, and gender. Answers were collected and analyzed anonymously. Worldwide female leadership was also queried via publicly available data. The survey was e-mailed to 1312 ILTS members, 199 responses (40.7% female) were collected from 38 countries (15.2% response rate). Almost half were surgeons (45.7%), 27.6% hepatologists and 26.6% anesthetists. Among 856 LT programs worldwide, 8.2% of leadership positions were held by females, and 22% of division chiefs were female across all specialties. Sixty-eight of respondents (34.7%) reported some form of discrimination during training or at their current position, presumably related to gender/sexual orientation (20.6%), race/country of origin (25.2%) and others (7.1%). Less than half (43.7%) received mentorship when discrimination occurred. An association between female responses and discrimination, differences in compensation, and job promotion was observed. This survey reveals alarmingly high rate of experience with racial and gender disparity, lack of mentorship, and very low rates of female leadership in the LT field and calls to action to equity and inclusion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".