Adapting the Admissions Interview During COVID-19: A Comparison of In-Person and Video-Based Interview Validity Evidence
Bibliographic record
Abstract
COVID-19 physical distancing limited many medical schools' abilities to conduct in-person interviews for the 2020 admissions cycle. The University of Toronto (U of T) Temerty Faculty of Medicine was already in the midst of its interview process, with two-thirds of applicants having completed the in-person modified personal interview (MPI). As the university and surrounding region were shut down, the shift was made in the middle of the application cycle to a semisynchronous video-based MPI interview (vMPI) approach. U of T undertook the development, deployment, and evaluation of the 2 approaches mid-admissions cycle. Existing resources and tools were used to create a tailored interview process with the assistance of applicants. The vMPI was similar in content and process to the MPI: a 4-station interview with each station mapped to attributes relevant to medical school success. Instead of live interviews, applicants recorded 5-minute responses to questions for each station using their own hardware. These responses were later assessed by raters asynchronously. Out of 627 applicants, 232 applicants completed the vMPI. Validity evidence was generated for the vMPI and compared with the MPI on the internal structure, relationship to other variables, and consequential validity, including applicant and interviewer acceptability. Overall, the vMPI demonstrated similar reliability and factor structure to the MPI. As with the MPI, applicant performance was predicted by nonacademic screening tools but not academic measures. Applicants' acceptance of the vMPI was positive. Most interviewers found the vMPI to be acceptable and reported confidence in their ratings. Continuing physical distancing concerns will require multiple options for admissions committees to select medical students. The vMPI is an example of a customized approach that schools can implement and may have advantages for selection beyond the COVID-19 pandemic. Future evaluation will examine additional validity evidence for the tool.
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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 | 0.389 | 0.599 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".