Gender and racial trends among neurology residents: an overview
Bibliographic record
Abstract
Diversification of academic medicine improves healthcare standards and patient outcomes. Gender and racial inequalities are major challenges faced by the healthcare system. This article reviews the trends of gender and racial disparity among residents of neurology. This retrospective analysis of the annual Accreditation Council for Graduate Medical Education Data Resource Books encompassed all residents at US neurology residency training programmes from the year 2007 to 2018. The representation of women steadily increased, with an absolute increase of 3% from the year 2007 to 2018. Although the absolute change (%) increased for the White race, Asian/Pacific Islander, Black/African Americans, there was a decrease seen in the Hispanic representation in neurology residents from the year 2011 to 2018. There was no change seen for the Native Americans/Alaskans. Our study concluded that gender and racial disparity persists in the recruitment of residents in neurology. This study highlights the need for targeted interventions to address gender and racial disparity among residents of neurology. Further studies are needed to explore etiological factors to address gender and racial disparity.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".