Abstract A075: Gender diversity in academic oncology programs
Bibliographic record
Abstract
Abstract Purpose: This study sought to examine gender diversity among academic faculty of medical oncology, hematology/oncology, and radiation oncology residency programs in the United States (U.S.), Canada, and Spain. Methods: Data from the Association of American Medical College (AAMC) were used to examine faculty composition of medical oncology, hematology/oncology, and radiation oncology departments in U.S. institutions for the years 1977 and 2017. For international data, public webpages listing post-graduate training programs in medical oncology and radiation oncology were examined and the genders of department heads were recorded. Results: In the U.S., in 1977, women made up 8%, 9%, and 7% of the total workforce among hematology/oncology, medical oncology, and radiation oncology faculty positions, respectively, compared to 44%, 40%, and 27%, respectively, in 2017. Regarding departmental leadership, in the U.S., 9% (8/89) of radiation oncology department chairs were women. In Canada, for radiation oncology, 11 department heads were men, 1 was a woman, and 1 department could not be determined (8-15% women). For medical oncology, 10 department heads were men, 3 women, and 2 were unknown (20-33% women). In Spain, 12 radiation oncology heads were women, 28 men, and 8 were unknown (25-42% women); for medical oncology, 14 department heads were women, 52 were men, and 11 were unknown (25-42% women). Conclusions: Women have historically increased in representation in the U.S. oncology workforce; however, they remain under-represented relative to the overall U.S. population. Women were also under-represented at the level of chair in the U.S., Canada, and Spain. Further research and efforts are needed to enlist and advance women in oncologic fields both nationally and internationally and understand barriers to training, practice, and advancement. Citation Format: Crystal S. Seldon, Awad A. Ahmed, Ricardo Llorente, Stella K. Yoo, Emma B. Holliday, Charles R. Thomas, Reshma Jagsi, Curtiland Deville Jr. Gender diversity in academic oncology programs [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr A075.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".