Perceptions of Equity and Inclusion in Acute Care Surgery
Bibliographic record
Abstract
OBJECTIVES AND BACKGROUND: The aim of this study was to characterize equity and inclusion in acute care surgery (ACS) with a survey to examine the demographics of ACS surgeons, the exclusionary or biased behaviors they witnessed and experienced, and where those behaviors happen. A major initiative of the Equity, Quality, and Inclusion in Trauma Surgery Practice Ad Hoc Task Force of the Eastern Association for the Surgery of Trauma was to characterize equity and inclusion in ACS. To do so, a survey was created with the above objectives. METHODS: A cross-sectional, mixed-methods anonymous online survey was sent to all EAST members. Closed-ended questions are reported as percentages with a cutoff of α = 0.05 for significance. Quantitative results were analyzed focusing on mistreatment and bias. RESULTS: Most respondents identified as white, non-Hispanic and male. In the past 12 months, 57.5% of females witnessed or experienced sexual harassment, whereas 48.6% of surgeons of color witnessed or experienced racial/ethnic discrimination. Sexual harassment, racial/ethnic prejudice, or discrimination based on sexual orientation/sex identity was more frequent in the workplace than at academic conferences or in ACS. Females were more likely than males to report unfair treatment due to age, appearance or sex in the workplace and ACS (P ≤ 0.002). Surgeons of color were more likely than white, non-Hispanics to report unfair treatment in the workplace and ACS due to race/ethnicity (P < 0.001). CONCLUSIONS: This is the first survey of ACS surgeons on equity and inclusion. Perceptions of bias are prevalent. Minorities reported more inequity than their white male counterparts. Behavior in the workplace was worse than at academic conferences or ACS. Ensuring equity and inclusion may help ACS attract and retain the best and brightest without fear of unfair treatment.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".