The Relationship between Interviewers’ Characteristics and Ratings Assigned during a Multiple Mini-Interview
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To assess the consistency of ratings assigned by health sciences faculty members relative to community members during an innovative admissions protocol called the Multiple Mini-Interview (MMI). METHOD: A nine-station MMI was created and 54 candidates to an undergraduate MD program participated in the exercise in Spring 2003. Three stations were staffed with a pair of faculty members, three with a pair of community members, and three with one member of each group. Raters completed a four-item evaluation form. All participants completed post-MMI questionnaires. Generalizability Theory was used to examine the consistency of the ratings provided within each of these three subgroups. RESULTS: The overall test reliability was found to be .78 and a Decision Study suggested that admissions committees should distribute their resources by increasing the number of interviews to which candidates are exposed rather than increasing the number of interviewers within each interview. Divergence of ratings was greater within the pairing of community member to faculty member and least for pairings of community members. Participants responded positively to the MMI. CONCLUSION: The MMI provides a reliable protocol for assessing the personal qualities of candidates by accounting for context specificity with a multiple sampling approach. Increasing the heterogeneity of interviewers may increase the heterogeneity of the accepted group of candidates. Further work will determine the extent to which different groups of raters provide equally valid (albeit different) judgments.
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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 | 0.001 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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