Simulation in admissions interviews: applicant experiences and programmatic performance prediction
Bibliographic record
Abstract
Background: Admissions interviews are frequently used to assess personal and interpersonal attributes required for successful medical practice. Using simulation in interviews to engage applicants in realistic medical scenarios to assess these attributes is novel. This study evaluates applicant perceptions of simulation within multiple mini-interviews (MMI) and reports on subsequent student program performance. Methods: Physician assistant (PA) program applicants were invited to complete an anonymous post-interview survey that included one free-response question about their admissions experience. We chose to qualitatively analyze the free-response question. Additionally, success metrics of students who experienced simulation-based MMI were compared to prior cohorts who were admitted using traditional interviews. Results: Applicants undergoing simulation-based interviews in MMI had decreased incidences of major professionalism events, greater on-time program progression, and similar board pass rates compared to applicants who experienced traditional interviews. Several themes, highlighting the applicants' varied responses to the simulation-based MMI, emerged including showcasing strengths and passion, feelings of fairness, accessing program faculty, and impacts on certainty. Conclusions: The use of simulation in admissions interviews is a valuable tool for assessing an applicant's personal attributes in a clinical setting. Applicants admitted using simulation had improved programmatic performance compared to applicants admitted using traditional interviews. Applicants' perceptions of simulation in interviews are helpful when designing the admissions experience.
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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 | 0.023 |
| 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) | 0.055 | 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".