Does an Emotional Intelligence Test Correlate With Traditional Measures Used to Determine Medical School Admission?
Bibliographic record
Abstract
BACKGROUND: As medical school admission committees are giving increased consideration to noncognitive measures, this study sought to determine how emotional intelligence (EI) scores relate to other traditional measures used in the admissions process. METHOD: EI was measured using an ability-based test (Mayer-Salovey-Caruso Emotional Intelligence Test, or MSCEIT) in two consecutive cohorts of medical school applicants (2006 and 2007) qualifying for the admission interview. Pearson correlations between EI scores and traditional measures (i.e., weighted grade point average [wGPA], autobiographical sketch scores, and interview scores) were calculated. RESULTS: Of 659 applicants, 68% participated. MSCEIT scores did not correlate with traditional measures (r = -0.06 to 0.09, P > .05), with the exception of a small correlation with wGPA in the 2007 cohort (r = -0.13, P < .05). CONCLUSIONS: The lack of substantial relationships between EI scores and traditional medical school admission measures suggests that EI evaluates a construct fundamentally different from traits captured in our admission process.
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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 | 0.001 | 0.034 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 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".