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 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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".