‘Click, I Guess I’m Done’: Applicants’ and Assessors’ Experiences Transitioning to a Virtual Multiple Mini Interview Format
Bibliographic record
Abstract
Introduction: During the COVID-19 pandemic, medical schools were forced to suspend in-person interviews and transition to a virtual Multiple Mini Interview (vMMI) format. MMIs typically comprise multiple short assessments overseen by assessors, with the aim of measuring a wide range of non-cognitive competencies. The adaptation to vMMI required medical schools to make swift changes to their MMI structure and delivery. In this paper, we focus on two specific groups greatly impacted by the decision to transition to vMMIs: medical school applicants and MMI assessors. Methods: We conducted an interpretive qualitative study to explore medical school applicants' and assessors' experiences transitioning to an asynchronous vMMI format. Ten assessors and five medical students from one Canadian medical school participated in semi-structured interviews. Data was analyzed using a thematic analysis framework. Results: Both applicants and assessors shared a mutual feeling of longing and nostalgia for an interview experience that, due to the pandemic, was understandably adapted. The most obvious forms of loss experienced - albeit in different ways - were: 1) human connection and 2) missed opportunity. Applicants and assessors described several factors that amplified their grief/loss response. These were: 1) resource availability, 2) technological concerns, and 3) the virtual interview environment. Discussion: While virtual interviewing has obvious advantages, we cannot overlook that asynchronous vMMIs do not lend themselves to the same caliber of interaction and camaraderie as experienced in in-person interviews. We outline several recommendations medical schools can implement to enhance the vMMI experience for applicants and assessors.
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".