The State of Diversity in Academic Plastic Surgery Faculty across North America
Bibliographic record
Abstract
BACKGROUND: Gender and racial disparity is prevalent in all surgical subspecialties with women and racial groups historically underrepresented in academic plastic surgery. This study evaluated gender and racial profiles of academic plastic surgery faculty in North America and correlated both with research productivity and its effect on academic ranks of faculty in plastic surgery. METHODS: In this cross-sectional study, we compiled a list of accredited medical schools that offer plastic surgery training for residency. Data were collected on demographics, academic rank, and research output using the Doximity, LinkedIn, and Scopus databases. Data analyses were performed with a Mann-Whitney U test and a Kruskal-Wallis test. RESULTS: Women who were black, indigenous, and/or other color occupied only 6.25% of plastic surgery faculty leadership positions in North America. There are more women and underrepresented minorities in leadership positions in Canada, when compared with the USA, relative to each country's demographic. In both countries, women and underrepresented minority plastic surgeons had fewer publications, citations, and years of active research. Interestingly, having women in leadership positions was associated with a higher number of women faculty members. CONCLUSIONS: Gender and racial disparity exist in academic plastic surgery in North America. Several changes are required in order for women and underrepresented minorities in medicine to have an equal chance at career advancement. Better representation and diverse leadership have the potential to bring about equity, diversity, and inclusion in academic plastic surgery.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".