Validity Of Admissions Measures in Predicting Performance Outcomes: A Comparison of Those Who Were and Were not Accepted at McMaster
Bibliographic record
Abstract
BACKGROUND: In typical validity studies, regression analyses are used to examine the relation between admissions measures and subsequent performance. This approach is problematic as it generally yields low correlation coefficients, which are difficult to interpret. Further, it leaves unanswered the question of how those applicants rejected by the process would fare had they been admitted. PURPOSE: This study examines the validity of the admissions measures used to assess non-cognitive qualities at McMaster's Medical School in a unique manner. METHODS: Three cohorts: (a) those offered an admission on the first round, (b) those offered an admission on the second round and (c) those rejected by McMaster, but accepted to another Canadian medical school were compared on admissions evaluations and licencing examination performance. RESULTS: The results indicate that although the scores of those who were offered an admission were significantly greater than those rejected by McMaster on each of the admission tools, licencing examination performance was comparable. CONCLUSIONS: These results are consistent with a previous regression-based validity study and indicate the need for closer examination of admissions tools.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".