The Intersection of Race and Sex: A New Perspective Into Diversity Trends in Orthopaedic Surgery
Bibliographic record
Abstract
INTRODUCTION: Studies on diversity in orthopaedic surgery have exclusively examined challenges from a race or sex perspective. This study examines trends in the diversity of entering orthopaedic surgery residents from the intersection of race and sex. METHODS: The American Association of Medical Colleges was queried for individuals entering orthopaedic surgery residencies in the United States from 2001 to 2020. Deidentified data on self-reported sex and race were collected. Proportions by the intersection of sex and race were calculated for 5-year intervals. RESULTS: From 2001 to 2020, most of the new female residents identified as White (mean, 71.0%). The average proportion of White female residents was lower in 2016 to 2020 than in 2001 to 2005 (71.0% vs. 73.2%) but higher than that in 2011 to 2015 (66.8%). The 2016 to 2020 average was lower than that of 2001 to 2005 for those who identified as Asian (11.7% vs. 14.9%), Black (4.1% vs. 4.8%), Hispanic (3.0% vs. 4.4%), and American Indian/Alaska Native (0.0% vs. 1.5%). Most of the new male orthopaedic surgery residents from 2001 to 2020 identified as White (mean, 74.1%), but the average decreased across every 5-year interval from 2001 to 2005 (76.1%) to 2016 to 2020 (71.1%). The 2016 to 2020 average was lower than that of 2001 to 2005 for those who identified as Asian (12.2% vs. 13.6%), Black (3.5% vs. 4.2%), Hispanic (3.0% vs. 3.4%), American Indian/Alaska Native (0.0% vs. 0.6%), and Native Hawaiian/Other Pacific Islander (0.1% vs. 0.3%). In 2020, White male residents made up to 54.2% of new residents. White female residents were the second highest group represented (12.1%). CONCLUSION: Increases in representation were observed for some subgroups of new orthopaedic surgery residents from 2001 to 2020. Although the proportion of both White female and male residents decreased by 11.5% during the 20-year study period, these individuals still made up most of the trainees in 2020. These results underscore the need for conversations and recruitment practices to take into consideration the intersectionality of identities.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".