S1431 Encouraging Diversity in Transplant Hepatology Fellowship Program Directorship: A Pressing Matter
Bibliographic record
Abstract
Introduction: Diversity in medicine has become an important focus due to studies showing the positive effects of race-concordant visits on patient outcomes, adherence, and satisfaction, resulting in more efficient and cost-effective care. While diversity among residency and fellowship program directors has been studied in other fields such as transplant surgery, cardiovascular disease, and psychiatry, little is known about the diversity within niche fellowship programs such as transplant hepatology. This study aims to investigate the demographic information of program directors in transplant hepatology fellowship programs. Methods: We identified transplant hepatology fellowship programs and their program directors from the American College of Gastroenterology website. Multiple reviewers compiled demographic and training information from internet searches, which was collected in Microsoft Excel and analyzed using chi-square analysis. Results: Our study analyzed data from 72 program directors, with 61.11% being male. Among the program directors, 55.6% were non-Hispanic White, 36.11% were Asian, while Hispanics and Blacks represented 5.56% and 4.17%, respectively. Our analysis also found that male program directors were largely non-hispanic white (72.72%) and were significantly more likely to be Professors (P = 0.045). Conclusion: Our findings indicate that transplant hepatology fellowship programs are primarily led by male and non-Hispanic White physicians, with female representation in leadership roles comparable to their membership in the workforce. However, we found a lack of diversity among both male and female underrepresented minorities in program director positions. Our study suggests that transplant hepatology fellowship program leadership lacks gender and racial diversity, with a particular underrepresentation of URM individuals. To attract underrepresented medical students and residents, it is critical to make meaningful efforts to improve diversity and ensure equitable representation of leaders. Future research should focus on identifying barriers to diversity within the field and developing strategies to build a more inclusive workforce while addressing existing leadership inequities (Table 1). Table 1. - Demographic data on all fellowship program directors Characteristic Fellowship Program Directors n = 72 % Credentials Additional Degree or Distinction 20 27.78% PhD 2 2.78% MBA 2 2.78% MPH 4 5.56% MS 7 9.72% FACG* 7 9.72% FAASLD** 12 16.67% Sex Male 44 61.11% Female 28 38.89% Race Non-Hispanic White 39 55.56% Black 3 4.17% Asian 26 36.11% Hispanic 4 5.56% Medical Education IMG*** 16 22.22% Canada 5 6.94% Academic Professor 15 20.83% Associate Professor 27 37.50% Assistant Professor 16 22.22% None 14 19.44% *Fellow of The American College of Gastroenterology. **Fellow of The American Association for the Study of Liver Diseases. ***International medical graduate.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".