Approaches to ensure an equitable and fair admissions process for medical training
Bibliographic record
Abstract
There has been considerable discussion of how best to address racial and ethnic disparities in health outcomes, both globally and specifically in the United States. Increasing diversity among future clinicians and physician-scientists has been identified as a key strategy for addressing and correcting health disparities among underrepresented populations. Increasingly, medical schools, the institutions that train clinicians, have embraced the practice of holistic review for evaluating applicants and virtually all medical schools have reported contributing to a diverse physician workforce as an important aspect of their educational mission. Yet despite these goals and practices, relatively little progress has been made in diversifying the workforce and achieving equitable health outcomes. Here we present a framework for centering equity in medical school admissions that focuses on equity-based recruiting, admissions standards, selection and support and present a number of promising examples and universally applicable strategies that medical schools can potentially implement given their unique missions, goals, priorities, and resources. Anachebe et al. discuss how to center equity in medical school admissions by presenting an equity-based framework that focuses on recruiting, standards, selection and support. Their recommended strategies are universally applicable across training programs and are accompanied by a number of promising examples.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".