Gender and Racial Trends Among Vascular Neurology Fellowship Programs: By Design or By Default
Bibliographic record
Abstract
Introduction Benefits of increasing diversity in teams include the addition of different perspectives leading to increased innovation and creativity, faster problem solving, improved workforce morale, and reduced burnout leading to improved patient outcomes. This article reviewed the trend of gender and racial disparity in vascular neurology fellowship programs. Methods We retrospectively analyzed the data extracted from the Accreditation Council for Graduate Medical Education (ACGME)'s annual Data Resource Books from 2007 to 2019. ACGME cataloged gender as men and women and race/ethnicity was categorized as White/Non-Hispanic, Asian or Pacific Island, Hispanic, Black/Non-Hispanic, Native American/Alaskan, others, and unknown. Counts, proportions, relative, and absolute percentage changes were calculated to highlight trends in resident appointments over time and across the specialty of vascular neurology. Results The representation of females increased steadily; with a relative increase of 11.78% from the year 2007 to 2019. Race/ethnicity was reported starting from the year 2011. When averaged across the nine-year study period, 35% of the study sample was White (Non-Hispanic), followed by Asian/Pacific Islanders at 25%. The representation of Hispanics was 4.8%, Black/African Americans were 3%, Native Americans/ Alaskans were 0.23% and Others were 13% of the total study population. For 17.7% of the fellows, racial data were not known and was categorized as Unknown racial distribution. Conclusion Our study concludes that gender and racial disparity persists within the fellowship programs of vascular neurology. Effective strategies at individual, administrative, and national levels are needed to engage women and under-represented minorities in vascular neurology as a career choice.
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 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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".