Medical Student Socioeconomic Disadvantage, Self-Designated Disadvantage, and Subsequent Academic Performance
Bibliographic record
Abstract
We previously reported that medical school matriculants with higher scores on a continuous measure of socioeconomic disadvantage (SED) had worse academic performance than those with lower scores. Analyses examining performance concurrently by SED and self-designated disadvantage (SDA) are lacking, an important gap since SDA may reflect perceptions only partly shaped by SED. We examined the associations of the four possible combinations of SED and SDA categories-SED+/SDA+, SED+/SDA-, and SED-/SDA+ (versus SED-/SDA-as reference)-with U.S. Medical Licensing Examination (USMLE) Step 1 and 2 Clinical Knowledge performance and third-year clerkship Honors at one medical school. USMLE scores were lower than reference for SED+/SDA+ and SED-/SDA+ (but not SED+/SDA-) students. SED+/SDA+, SED+/SDA-, and SED-/SDA+ students all received fewer Honors than reference. The findings indicate SED and SDA each predict different features of medical school performance, suggesting avenues for enhancing disadvantaged students' success and the representativeness of the physician workforce.
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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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".