Medical School Admissions: Revisiting the Veracity and Independence of Completion of an Autobiographical Screening Tool
Bibliographic record
Abstract
BACKGROUND: Some form of candidate-written autobiographical submission (ABS) is commonly used before interviews to screen candidates to medical school on the basis of their noncognitive characteristics. However, confidence in the validity of these measures has been questioned. METHOD: In 2005, applicants to McMaster University completed an off-site ABS before being interviewed and an on-site ABS at interview. Five off-site ABS questions were completed, plus eight on-site questions. On-site ABS questions were answered in variable timing conditions. ABS ratings were compared across sites and time allowed for completion. RESULTS: Off-site ABS ratings were higher than on-site ratings, and the two sets of ratings were uncorrelated with one another. On-site ABS ratings increased with increased time allowed for completion, but the reliability of the measure was unaffected by this variable. CONCLUSIONS: Confidence that candidates independently answer preinterview ABS questions is weak. To improve ABS validity, modification of the current Web-based submission format warrants consideration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.194 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".