And Miles to Go Before We Sleep: EAST Diversity and Inclusivity Progress and Remaining Challenges.
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the diversity, equity, and inclusion landscape in academic trauma surgery and the EAST organization. SUMMARY BACKGROUND DATA: In 2019, the Eastern Association for the Surgery of Trauma (EAST) surveyed its members on equity and inclusion in the #EAST4ALL survey and assessed leadership representation. We hypothesized that women and surgeons of color (SOC) are underrepresented as EAST members and leaders. METHODS: Survey responses were analyzed post-hoc for representation of females and SOC in academic appointments and leadership, EAST committees, and the EAST board, and compared to the overall respondent cohort. EAST membership and board demographics were compared to demographic data from the Association of American Medical Colleges. RESULTS: Of 306 respondents, 37.4% identified as female and 23.5% as SOC. There were no significant differences in female and SOC representation in academic appointments and EAST committees compared to their male and white counterparts. In academic leadership, females were underrepresented ( P < 0.0001), whereas SOC were not ( P = 0.08). Both females and SOC were underrepresented in EAST board membership ( P = 0.002 and P = 0.043, respectively). Of EAST's 33 presidents, 3 have been white women (9%), 2 have been Black, non-African American men (6%), and 28 (85%) have been white men. When compared to 2017 AAMC data, women are well-represented in EAST's 2020 membership ( P < 0.0001) and proportionally represented on EAST's 2019-2020 board ( P > 0.05). CONCLUSIONS: The #EAST4ALL survey suggests that women and SOC may be underrepresented as leaders in academic trauma surgery. However, lack of high-quality demographic data makes evaluating representation of structurally marginalized groups challenging. National trauma organizations should elicit data from their members to re-assess and promote the diversity landscape in trauma surgery.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".