Validity of Admissions Measures in Predicting Performance Outcomes: The Contribution of Cognitive and Non-Cognitive Dimensions
Bibliographic record
Abstract
BACKGROUND: Admissions committees face the daunting task of selecting a small number of candidates who are most likely to succeed in medical school from a large pool of seemingly suitable applicants. While numerous studies have shown moderate correlations among measures of academic performance, predictors of the non-cognitive domain (e.g. interpersonal, communication, ethical) remain elusive, in part because of the absence of a sound criterion measure. PURPOSE: We examined the utility of several cognitive and non-cognitive criteria used in the admissions processes in predicting both cognitive and non-cognitive dimensions of the licencing examinations of the Medical Council of Canada (LMCC). METHODS: Predictors included: undergraduate GPA, undergraduate science GPA, an autobiographical letter, scores from a simulated tutorial, a personal interview and the MCAT. Of specific interest was the relation between measures of communication and problem-exploration skills as assessed during the admissions process and Part II of the LMCC Examination, a multi-station OSCE. RESULTS: Undergraduate GPAs were found to have the most utility in predicting both academic and clinical performance. Scores derived from the simulated tutorial did not predict future performance. The MCAT Verbal Reasoning score and the personal interview were found to be useful in predicting communication skills on the LMCC Part II. CONCLUSIONS: The results have implications for any school that uses the interview as an admissions tool.
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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 | 0.020 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".