Does Emotional Intelligence at Medical School Admission Predict Future Academic Performance?
Bibliographic record
Abstract
PURPOSE: Medical school admissions committees are increasingly considering noncognitive measures like emotional intelligence (EI) in evaluating potential applicants. This study explored whether scores on an EI abilities test at admissions predicted future academic performance in medical school to determine whether EI could be used in making admissions decisions. METHOD: The authors invited all University of Ottawa medical school applicants offered an interview in 2006 and 2007 to complete the Mayer-Salovey-Caruso EI Test (MSCEIT) at the time of their interview (105 and 101, respectively), then again at matriculation (120 and 106, respectively). To determine predictive validity, they correlated MSCEIT scores to scores on written examinations and objective structured clinical examinations (OSCEs) administered during the four-year program. They also correlated MSCEIT scores to the number of nominations for excellence in clinical performance and failures recorded over the four years. RESULTS: The authors found no significant correlations between MSCEIT scores and written examination scores or number of failures. The correlations between MSCEIT scores and total OSCE scores ranged from 0.01 to 0.35; only MSCEIT scores at matriculation and OSCE year 4 scores for the 2007 cohort were significantly correlated. Correlations between MSCEIT scores and clinical nominations were low (range 0.12-0.28); only the correlation between MSCEIT scores at matriculation and number of clinical nominations for the 2007 cohort were statistically significant. CONCLUSIONS: EI, as measured by an abilities test at admissions, does not appear to reliably predict future academic performance. Future studies should define the role of EI in admissions decisions.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".