Emotional intelligence in orthopedic surgery residents
Bibliographic record
Abstract
BACKGROUND: Emotional intelligence (EI) is the ability to understand and manage emotions in oneself and others. It was originally popularized in the business literature as a key attribute for success that was distinct from cognitive intelligence. Increasing focus is being placed on EI in medicine to improve clinical and academic performance. Despite the proposed benefits, to our knowledge, there have been no previous studies on the role of EI in orthopedic surgery. We evaluated baseline data on EI in a cohort of orthopedic surgery residents. METHODS: We asked all orthopedic surgery residents at a single institution to complete an electronic version of the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). We used completed questionnaires to calculate total EI scores and 4 branch scores. Data were analyzed according to a priori cutoff values to determine the proportion of residents who were considered competent on the test. Data were also analyzed for possible associations with age, sex, race and level of training. RESULTS: Thirty-nine residents (100%) completed the MSCEIT. The mean total EI score was 86 (maximum score 145). Only 4 (10%) respondents demonstrated competence in EI. Junior residents (p = 0.026), Caucasian residents (p = 0.009) and those younger than 30 years (p = 0.008) had significantly higher EI scores. CONCLUSION: Our findings suggest that orthopedic residents score low on EI based on the MSCEIT. Optimizing resident competency in noncognitive skills may be enhanced by dedicated EI education, training and testing.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".