A predictive validity study of medical judgment vignettes to assess students’ noncognitive attributes: A 3-year prospective longitudinal study
Bibliographic record
Abstract
BACKGROUND: The admissions interview still remains the most common approach used to describe candidates' noncognitive attributes for medical school. AIM: In this prospective study, we have investigated the predictive validity of a semi-structured interview for admissions to medical school based on medical judgment vignettes: (1) ethical decision-making (moral), (2) relationships with patients and their families (altruistic), and (3) roles and responsibilities in professional relationships (dutiful). METHOD: A group of 26 medical students from the Class of 2007 participated in the interview process and provided their subsequent performance results from clerkship 3 years later. RESULTS: Inter-rater reliability of the scored interviews was high (kappa = 0.96). Our results provided evidence for both convergent and divergent predictive validity. Medical judgment vignettes scores correlated significantly with seven mandatory clerkship rotation in-training evaluation reports (r = 0.39, p < 0.05; to r = 0.55, p < 0.01). CONCLUSION: This semi-structured interview based on clearly defined and scored medical judgment vignettes that focus on the assessment of medical students' noncognitive attributes is promising for student's selection into medical school. The high reliability and evidence of predictive validity of clinical performance over a 3-year period suggests a workable approach to the assessment of 'compelling personal characteristics' beyond merely cognitive variables.
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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 | 0.023 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".