Equity on the frontlines of trauma surgery: An #EAST4ALL roundtable
Bibliographic record
Abstract
BACKGROUND: Inequity exists in surgical training and the workplace. The Eastern Association for the Surgery of Trauma (EAST) Equity, Quality, and Inclusion in Trauma Surgery Ad Hoc Task Force (EAST4ALL) sought to raise awareness and provide resources to combat these inequities. METHODS: A study was conducted of EAST members to ascertain areas of inequity and lack of inclusion. Specific problems and barriers were identified that hindered inclusion. Toolkits were developed as resources for individuals and institutions to address and overcome these barriers. RESULTS: Four key areas were identified: (1) harassment and discrimination, (2) gender pay gap or parity, (3) implicit bias and microaggressions, and (4) call-out culture. A diverse panel of seven surgeons with experience in overcoming these barriers either on a personal level or as a chief or chair of surgery was formed. Four scenarios based on these key areas were proposed to the panelists, who then modeled responses as allies. CONCLUSION: Despite perceived progress in addressing discrimination and inequity, residents and faculty continue to encounter barriers at the workplace at levels today similar to those decades ago. Action is needed to address inequities and lack of inclusion in acute care surgery. The EAST is working on fostering a culture that minimizes bias and recognizes and addresses systemic inequities, and has provided toolkits to support these goals. Together, we can create a better future for all of us.
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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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".