Evaluating a Global Assessment Measure Created by Standardized Patients for the Multiple Mini Interview in Medical School Admissions: Mixed Methods Study
Bibliographic record
Abstract
Background Standardized patients (SPs) are essential stakeholders in the multiple mini interviews (MMIs) that are increasingly used to assess medical school applicants’ interpersonal skills. However, there is little evidence for their inclusion in the development of instruments. Objective This study aimed to describe the process and evaluate the impact of having SPs co-design and cocreate a global measurement question that assesses medical school applicants’ readiness for medical school and acceptance status. Methods This study used an exploratory, sequential, and mixed methods study design. First, we evaluated the initial MMI program and determined the next quality improvement steps. Second, we held a collaborative workshop with SPs to codevelop the assessment question and response options. Third, we evaluated the created question and the additional MMI rubric items through statistical tests based on 1084 applicants’ data from 3 cohorts of applicants starting in the 2018-2019 academic year. The internal reliability of the MMI was measured using a Cronbach α test, and its prediction of admission status was tested using a forward stepwise binary logistic regression. Results Program evaluation indicated the need for an additional quantitative question to assess applicant readiness for medical school. In total, 3 simulation specialists, 2 researchers, and 21 SPs participated in a workshop leading to a final global assessment question and responses. The Cronbach α’s were >0.8 overall and in each cohort year. The final stepwise logistic model for all cohorts combined was statistically significant (P<.001), explained 9.2% (R2) of the variance in acceptance status, and correctly classified 65.5% (637/972) of cases. The final model consisted of 3 variables: empathy, rank of readiness, and opening the encounter. Conclusions The collaborative nature of this project between stakeholders, including nonacademics and researchers, was vital for the success of this project. The SP-created question had a significant impact on the final model predicting acceptance to medical school. This finding indicates that SPs bring a critical perspective that can improve the process of evaluating medical school applicants.
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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 | 0.024 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.017 | 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".