Ethnic and Racial Differences in Ratings in the Medical Student Standardized Letters of Evaluation (SLOE)
Bibliographic record
Abstract
Background: The Standardized Letter of Evaluation (SLOE) stratifies the assessment of emergency medicine (EM) bound medical applicants. However, bias in SLOE, particularly regarding race and ethnicity, is an underexplored area. Objective: This study aims to assess whether underrepresented in medicine (UIM) and non-UIM applicants are rated differently in SLOE components. Methods: This was a cross-section study of EM-bound applicants across 3 geographically distinct US training programs during the 2019-2020 application cycle. Using descriptive and regression analyses, we examine the differences between UIM applicants and non-UIM applicants for each of the SLOE components: 7 qualifications of an EM physician (7QEM), global assessment (GA) rating, and projected rank list (RL) position. Results: Out of a combined total of 3759, 2002 (53.3%) unique EM-bound applicants were included. UIM applicants had lower ratings for each of the 7QEM questions, GA, and RL positions. Compared to non-UIM applicants, only some of the 7QEM components: "Work ethic and ability to assume responsibility," "Ability to work in a team, and "Ability to communicate a caring nature," were associated with their SLOE. "Commitment to EM" correlated more with GA for UIM than for non-UIM applicants. Conclusions: This study shows a difference in SLOE rating, with UIM applicants receiving lower ratings than non-UIM applicants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".