Reducing Medical School Admissions Disparities in an Era of Legal Restrictions: Adjusting for Applicant Socioeconomic Disadvantage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A diverse physician workforce is needed to increase access to care for underserved populations, particularly as the Affordable Care Act expands insurance coverage. Yet legal restrictions constrain the extent to which medical schools may use race/ethnicity in admissions decisions. We conducted simulations using academic metrics and socioeconomic data from applicants to a California public medical school from 2011 to 2013. The simulations systematically adjusted medical school applicants' academic metrics for socioeconomic disadvantage. We found that socioeconomic and under-represented minority disparities in admissions could be eliminated while maintaining academic readiness. Adjusting applicant academic metrics using socioeconomic information on medical school applications may be a race-neutral means of increasing the socioeconomic and racial/ethnic diversity of the physician workforce.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it