Racial and gender disparities in neurology
Bibliographic record
Abstract
INTRODUCTION: The USA is a diverse society with representation from different ethnic and racial backgrounds, resulting in under-represented minorities (URMs) in various specialties of medicine. Our objective was to find the statistical ratio of URMs in the academic faculty of neurology. METHODS: This was a retrospective analysis of the American Association of Medical College database. The database covered neurology faculty members from 2006 to 2017. RESULTS: This study shows a significant change in racial representation in faculty ranks over the last 12 years. At chairperson rank, white people decreased from 86.4% to 79.8% whereas Asian, Hispanic and multiple races (non-Hispanic) simultaneously increased from 6.4% to 9.3%, 0.9% to 3.1% and 1.8% to 4.7%, respectively. At the professor rank, white people decreased from 87.4% to 81.6%, while Asians and Hispanics increased from 7.1% to 10.5% and from 0.7% to 2.1%, respectively. At the rank of associate professor, white people decreased from 81.1% to 68.3% whereas Asians, Hispanics and unknown races increased from 10.3% to 19.0%, 1.6% to 3.1% and from 2.1% to 3.5%, respectively. For the rank of assistant professor, white people decreased from 64.7% to 56.9% and Asians increased from 20.5% to 25.9%. Gender differences (men vs women) for the ranks of chairperson, professor, associate professor, assistant professor and instructors were 90.3% and 9.7%, 83.1% and 16.9%, 67.1% and 32.9%, 56.8% and 43.2%, and 48.1% and 51.9%, respectively. CONCLUSION: Over a period of 12 years the racial proportion in academic neurology has changed, but it is not proportionate to their respective increase in the population of the USA. Moreover, the portion of female faculty increased, but they are still under-represented in leadership roles. This racial and gender disparity can be addressed by well-planned interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".