Beyond cognitive measures: Empirical evidence supporting holistic medical school admissions practices and professional identity formation
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Background: Medical schools seek admissions methods that identify applicants who hold promise to become physicians who will navigate and shape the future medical landscape. The focus on traditional cognitive measures for admission has prompted calls for holistic admissions review during the past five years. Yet, empirical evidence linking selection measures to holistic admissions practices has not been fully established, including their relationship with professional identity formation over time. A non-cognitive admissions situational judgment screening test (CASPer) measuring personal and professional characteristics was added to the University of Illinois College of Medicine admissions process two years ago, as we implemented a new curriculum that emphasizes professional identity development. Purpose: This study examined associations among admissions measures (Medical College Admission Test [MCAT], grade point average [GPA], interview, and CASPer), and their predictive relationships with curricular measures of professional identity formation (Professional Identity Essay [PIE]) and moral reasoning (Defining Issues Test [DIT2]). Methods: Data were taken from two entering cohorts (n = 596; entering class of 2017 and 2018 across 3 regional sites). Correlations and regression analyses were used to examine associations between admissions and professional identity measures. Results: CASPer and in-person admissions interview ratings had significant positive correlations, suggesting that CASPer can contribute to effective screening processes. In addition, CASPer demonstrated statistically significant positive relationships with professional identity (CASPer and PIE, r=.10, p<.05) and a measure of moral reasoning (CASPer and DIT2 type indicator, r=.09, p<.05). Association between CASPer and PIE remained consistent, even after controlling for MCAT, interview, and GPA. Conclusion: Our institutional focus on professional identity formation has provided new ways to conceptualize students' readiness for medical school – demonstrated academic rigor as well as signs of professionalism, ethics, and motivation. Non-academic factors measured in situational judgment tests may promote better alignment of admissions practices and desired educational outcomes.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".