Effectiveness of medical school admissions criteria in predicting residency ranking four years later
Bibliographic record
Abstract
BACKGROUND: Medical schools across Canada expend great effort in selecting students from a large pool of qualified applicants. Non-cognitive assessments are conducted by most schools in an effort to ensure that medical students have the personal characteristics of importance in the practice of Medicine. We reviewed the ability of University of Toronto academic and non-academic admission assessments to predict ranking by Internal Medicine and Family Medicine residency programmes. METHODS: The study sample consisted of students who had entered the University of Toronto between 1994 and 1998 inclusive, and had then applied through the Canadian resident matching programme to positions in Family or Internal Medicine at the University of Toronto in their graduating year. The value of admissions variables in predicting medical school performance and residency ranking was assessed. RESULTS: Ranking in Internal Medicine correlated significantly with undergraduate grade point average (GPA) and the admissions non-cognitive assessment. It also correlated with 2-year objective structured clinical examination (OSCE) score, clerkship grade in Internal Medicine, and final grade in medical school. Ranking in Family Medicine correlated with the admissions interview score. It also correlated with 2nd-year OSCE score, clerkship grade in Family Medicine, clerkship ward evaluation in Internal Medicine and final grade in medical school. DISCUSSION: The results of this study suggest that cognitive as well as non-cognitive factors evaluated during medical school admission are important in predicting future success in Medicine. The non-cognitive assessment provides additional value to standard academic criteria in predicting ranking by 2 residency programmes, and justifies its use as part of the admissions process.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".