Modified Personal Interviews
Bibliographic record
Abstract
PURPOSE: Traditional admissions personal interviews provide flexible faculty-student interactions but are plagued by low inter-interview reliability. Axelson and Kreiter (2009) retrospectively showed that multiple independent sampling (MIS) may improve reliability of personal interviews; thus, the authors incorporated MIS into the admissions process for medical students applying to the University of Toronto's Leadership Education and Development Program (LEAD). They examined the reliability and resource demands of this modified personal interview (MPI) format. METHOD: In 2010-2011, LEAD candidates submitted written applications, which were used to screen for participation in the MPI process. Selected candidates completed four brief (10-12 minutes) independent MPIs each with a different interviewer. The authors blueprinted MPI questions to (i.e., aligned them with) leadership attributes, and interviewers assessed candidates' eligibility on a five-point Likert-type scale. The authors analyzed inter-interview reliability using the generalizability theory. RESULTS: Sixteen candidates submitted applications; 10 proceeded to the MPI stage. Reliability of the written application components was 0.75. The MPI process had overall inter-interview reliability of 0.79. Correlation between the written application and MPI scores was 0.49. A decision study showed acceptable reliability of 0.74 with only three MPIs scored using one global rating. Furthermore, a traditional admissions interview format would take 66% more time than the MPI format. CONCLUSIONS: The MPI format, used during the LEAD admissions process, achieved high reliability with minimal faculty resources. The MPI format's reliability and effective resource use were possible through MIS and employment of expert interviewers. MPIs may be useful for other admissions tasks.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.020 |
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".