Does Emotional Intelligence at medical school admission predict future licensing examination performance?
Bibliographic record
Abstract
Background: Medical school admissions committees are seeking alternatives to traditional academic measures when selecting students; one potential measure being emotional intelligence (EI). If EI is to be used as an admissions criterion, it should predict future performance. The purpose of this study is to determine if EI scores at admissions predicts performance on a medical licensure examination Methods: All medical school applicants to the University of Ottawa in 2006 and 2007 were invited to complete the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT v2.0) after their interview. Students were tracked through medical school into licensure and EI scores were correlated to their scores on the Medical Council of Canada Qualifying Examination (MCCQE) attempted between 2010 and 2014. Results: The correlation between the MSCEIT and the MCCQE Part I was r (200) = .01 p =. 90 The covariates of age and gender accounted for a significant amount of variance in MCCQE Part I scores (R2 = .10, p <.001, n=202) but the addition of the MSCEIT scores was not statistically significant (R2 change = .002, p=.56). The correlation between the MSCEIT and the MCCQE Part II was r(197) = .06, p = .41. The covariates of age and gender accounted for some variance in MCCQE Part II scores (R2 = .05, p = .007, n=199) but the addition of the MSCEIT did not (R2 change = .002 p =.55). Conclusion: The low correlations between EI and licensure scores replicates other studies that have found weak correlations between EI scores and tests administered at admissions and during medical school. These results suggest caution if one were to use EI as part of their admissions process.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".