Reliability of a Structured Interview for Admission to an Emergency Medicine Residency Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Interviews are most important in resident selection. Structured interviews are more reliable than unstructured ones. PURPOSE: We sought to measure the interrater reliability of a newly designed structured interview during the selection process to an Emergency Medicine residency program. METHODS: The critical incident technique was used to extract the desired dimensions of performance. The interview tool consisted of 7 clinical scenarios and 1 global rating. Three trained interviewers marked each candidate on all scenarios without discussing candidates' responses. Interitem consistency and estimates of variance were computed. RESULTS: Twenty-eight candidates were interviewed. The generalizability coefficient was 0.67. Removing the central tendency ratings increased the coefficient to 0.74. Coefficients of interitem consistency ranged from 0.64 to 0.74. CONCLUSIONS: The structured interview tool provided good although suboptimal interrater reliability. Increasing the number of scenarios improves reliability as does applying differential weights to the rating scale anchors. The latter would also facilitate the identification of those candidates with extreme ratings.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it