Effectiveness of medical school admissions criteria in predicting residency ranking four years later
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.046 | 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 it