Diversity by Race, Hispanic Ethnicity, and Sex of the United States Medical Oncology Physician Workforce Over the Past Quarter Century
Bibliographic record
Abstract
PURPOSE: To assess the medical oncology (MO) physician workforce diversity by race, Hispanic ethnicity, and sex, with attention to trainees. METHODS: Public registries were used to assess 2010 differences among MO practicing physicians, academic faculty, and fellows; internal medicine (IM) residents; and the US population, using binomial tests with P < .001 significance adjusting for multiple comparisons. Significant changes in fellow representation from 1986 to 2011 were assessed. RESULTS: Female representation as MO fellows (45.0%) was significantly increased compared with faculty (22.4%) and practicing physicians (27.4%); was no different than IM residents (44.7%, P = .853); and increased significantly, by 1.0% per year. Women were significantly underrepresented as practicing physicians, faculty, and fellows compared with the US population (50.8%). Traditionally underrepresented minorities in medicine (URM) were significantly underrepresented as practicing physicians (7.8%), faculty (5.7%), and fellows (10.9%), versus US population (30.0%). Hispanic MO fellows (7.5%) were increased compared with faculty (3.9%) and practicing physicians (4.1%); Black fellows (3.1%) were no different than faculty (1.8%, P = .0283) or practicing physicians (3.5%, P = .443). When comparing MO fellows versus IM residents, there were no differences for American Indians/Alaska Natives/Native Hawaiians/Pacific Islanders (0.3%, 0.6%, respectively, P = .137) and Hispanics (7.5%, 8.7%, P = .139), unlike Blacks (3.1%, 5.6%, P < .001). There has been no significant change in URM representation, with negligible changes every 5 years for American Indians/Alaska Natives/Native Hawaiians/Pacific Islanders (-0.1%), Blacks (-0.3%), and Hispanics (0.3%). CONCLUSIONS: Female fellow representation increased 1% per year over the quarter century indicating historical gains, whereas URM diversity remains unchanged. For Blacks alone, representation as MO fellows is decreased compared with IM residents, suggesting greater disparity in MO training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".