Medical School Admissions: Enhancing the Reliability and Validity of an Autobiographical Screening Tool
Bibliographic record
Abstract
BACKGROUND: Most medical school applicants are screened out preinterview. Some cognitive scores available preinterview and some noncognitive scores available at interview demonstrate reasonable reliability and predictive validity. A reliable preinterview noncognitive measure would relax dependence upon screening based entirely on cognitive tendencies. METHOD: In 2005, applicants interviewing at McMaster University's Michael G. DeGroote School of Medicine completed an offsite, noninvigilated, Autobiographical Submission (ABS) preinterview and another onsite, invigilated, ABS at interview. Traditional and new ABS scoring methods were compared, with raters either evaluating all ABS questions for each candidate in turn (vertical scoring-traditional method) or evaluating all candidates for each question in turn (horizontal scoring-new method). RESULTS: The new scoring method revealed lower internal consistency and higher interrater reliability relative to the traditional method. More importantly, the new scoring method correlated better with the Multiple Mini-Interview (MMI) relative to the traditional method. CONCLUSIONS: The new ABS scoring method revealed greater interrater reliability and predictive capacity, thus increasing its potential as a screen for noncognitive characteristics.
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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 | 0.026 | 0.075 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".