Reliability of a Structured Interview Scoring Instrument for a Canadian Postgraduate Emergency Medicine Training Program
Bibliographic record
Abstract
OBJECTIVES: To determine the reliability of scores assigned to interviews of medical students applying to an emergency medicine program. METHODS: A scoring instrument was derived based on faculty and resident input, institutional and national documents, and previous application procedures. Candidates were interviewed by four pairs of interviewers. Interviewers were asked to score the candidates on five visual analog scales (VASs) with objective anchors. Each interview assessed a unique candidate characteristic. All interviewers were given explicit instructions on scoring procedures and instrument use. The data were entered into an Excel database and transferred to SPSS, and reliabilities were measured with a two-way mixed-effect Cronbach's alpha. RESULTS: Forty applications were received for the 2002 residency entry year. Thirty-eight application packages were complete, and 16 candidates were interviewed. Data collection was complete for all 16. The average measure intraclass correlations for each individual interviewer across the five VASs ranged from 0.72 to 0.92 (mean, 0.85). The interrater reliability within the four interviews (personal characteristics, trainability, suitability for emergency medicine, and suitability for the specific training program) were low at 0.36, 0.59, 0.69, and 0.49. The overall reliability of the four interview scores was 0.83, and for the eight interviewer scores it was 0.86. CONCLUSIONS: The reliability of the overall interview scores was very high. The intraclass correlations for each interviewer's VAS scores were also high, but interrater correlations within interview teams were moderate and not higher than those across interview teams. This study suggests that an interview assessment instrument can be highly reliable overall and that interviewers base scores on an overall global impression.
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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 | 0.032 | 0.067 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".