Comparing 2 Approaches for the File Review of Residency Applications
Bibliographic record
Abstract
BACKGROUND: The residency selection process relies on subjective information in applications, as well as subjective assessment of applications by reviewers. This inherent subjectivity makes residency selection prone to poor reliability between those reviewing files. OBJECTIVES: We compared the interrater reliability of 2 assessment tools during file review: one rating applicant traits (ie, leadership, communication) and the other using a global rating of application elements (ie, curriculum vitae, reference letters). METHODS: Ten file reviewers were randomized into 2 groups, and each scored 7 general surgery applications from the 2019-2020 cycle. The first group used an element-based (EB) scoring tool, while the second group used a trait-based (TB) scoring tool. Feedback was collected, discrimination capacities were measured using variation in scores, and interrater reliability (IRR) was calculated using intraclass correlation (ICC) in a 2-way random effects model. RESULTS: Both tools identified the same top-ranked and bottom-ranked applicants; however, discrepancies were noted for middle-ranked applicants. The score range for the 5 middle-ranked applicants was greater with the TB tool (6.43 vs 3.80), which also demonstrated fewer tie scores. The IRR for TB scoring was superior to EB scoring (ICC [2, 5] = 0.82 vs 0.55). The TB tool required only 2 raters to achieve an ICC ≥ 0.70. CONCLUSIONS: Using a TB file review strategy can facilitate file review with improved reliability compared to EB, and a greater spread of candidate scores. TB file review potentially offers programs a feasible way to optimize and reflect their institution's core values in the process.
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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 | 0.160 | 0.367 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".