A survey of medical school aspirant perceptions of an unexpected lottery‐facilitated admissions adaptation
Bibliographic record
Abstract
INTRODUCTION: Due to the COVID-19 pandemic, the Undergraduate Medical Doctor (MD) Programme at McMaster University (Hamilton, Canada) was unable to run in-person medical school interviews in March 2020, prompting an alternate solution that maximised admission opportunities for Indigenous applicants, prioritised admission for those rated most highly in the interview determination process, and allocated subsequent offers via lottery. METHODS: A short survey was administered to applicants who had been offered an admissions interview and were subsequently impacted by the admissions adaptations. The survey elicited perceptions of the adaptation through Likert scale ratings and free-text responses. Survey data were analysed via a sequential (quantitative to qualitative) mixed-methods design. RESULTS: 196 of 552 potential participants completed the survey. Across quantitative and qualitative analyses, respondents reported that the adaptation had a negative impact on their professional development and personal life. Ratings of negative perception were greater for those who did not receive an offer than for those who accepted or declined an offer. Free text responses emphasised considerable criticism for the lottery portion of the adaptation and displeasure that efforts made in constructing applications were less relevant than anticipated. DISCUSSION: The negative responses to this unexpected change highlight the profound upstream impact admission policies have on the preapplication behaviours of aspiring medical students. The outcomes support a refined understanding of the value candidates place on the interview in appraising their own suitability for a career as a physician.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".