Extending the Interview to All Medical School Candidates—Computer-Based Multiple Sample Evaluation of Noncognitive Skills (CMSENS)
Bibliographic record
Abstract
BACKGROUND: Most medical school candidates are excluded without benefit of noncognitive skills assessment. Is development of a noncognitive preinterview screening test that correlates with the well-validated Multiple Mini-Interview (MMI) possible? METHOD: Study 1: 110 medical school candidates completed MMI and Computer-based Multiple Sample Evaluation of Noncognitive Skills (CMSENS)-eight 1-minute video-based scenarios and four self-descriptive questions, with short-answer-response format. Seventy-eight responses were audiotaped, 32 typewritten; all were scored by two independent raters. Study 2: 167 candidates completed CMSENS-eight videos, six self-descriptive questions, typewritten responses only, scored by two raters; 88 of 167 underwent the MMI. RESULTS: Results for overall test generalizability, interrater reliability, and correlation with MMI, respectively, were, for Study 1, audio-responders: 0.86, 0.82, 0.15; typewritten-responders: 0.72, 0.81, 0.51; and for Study 2, 0.83, 0.95, 0.46 (correlation with disattenuation was 0.60). CONCLUSIONS: Strong psychometric properties, including MMI correlation, of CMSENS warrant investigation into future widespread implementation as a preinterview noncognitive screening test.
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How this classification was reachedexpand
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 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.014 | 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 teacher head, 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".