Addressing the Diversity–Validity Dilemma Using Situational Judgment Tests
Bibliographic record
Abstract
PURPOSE: To examine the magnitudes of score differences across different demographic groups for three academic (grade point average [GPA], old Medical College Admission Test [MCAT], and MCAT 2015) and one nonacademic (situational judgment test [SJT]) screening measures and one nonacademic (multiple mini-interview [MMI]) interview measure (analysis 1), and the demographic implications of including an SJT in the screening stage for the pool of applicants who are invited to interview (analysis 2). METHOD: The authors ran the analyses using data from New York Medical College School of Medicine applicants from the 2015-2016 admissions cycle. For analysis 1, effect sizes (Cohen d) were calculated for GPA, old MCAT, MCAT 2015, CASPer (an online SJT), and MMI. Comparisons were made across gender, race, ethnicity (African American, Hispanic/Latino), and socioeconomic status (SES). For analysis 2, a series of simulations were conducted to estimate the number of underrepresented in medicine (UIM) applicants who would have been invited to interview with different weightings of GPA, MCAT, and CASPer scores. RESULTS: A total of 9,096 applicants were included in analysis 1. Group differences were significantly smaller or reversed for CASPer and MMI compared with the academic assessments (MCAT, GPA) across nearly all demographic variables/indicators. The simulations suggested that a higher weighting of CASPer may help increase gender, racial, and ethnic diversity in the interview pool; results for low-SES applicants were mixed. CONCLUSIONS: The inclusion of an SJT in the admissions process has the potential to widen access to medical education for a number of UIM groups.
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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 | 0.001 | 0.004 |
| 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) | 0.014 | 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".