Cross-Sectional Assessment of the Emotional Intelligence of Fourth-Year Veterinary Students and Veterinary House Officers in a Teaching Hospital
Bibliographic record
Abstract
Emotional intelligence (EI) is the recognition and management of emotions. This skill set is important to work relationships and professional success. In this cross-sectional, observational study, we investigated EI scores of 4th-year veterinary students, interns, and residents in a teaching hospital, using a psychometric tool with professional population norms. Participants' EI scores were compared with professional norms and between the sample groups. Scores were examined on the basis of demographics and residency program type. Twenty-four 4th-year students and 43 interns and residents completed the survey. Total, composite, and subscale scores for all groups were lower than professional means. We noted no statistically significant differences in EI scores between training levels, but evaluation of effect sizes showed a medium negative effect of higher training levels on Self-Perception Composite, Self-Regard, Emotional Expression, Interpersonal Composite, Flexibility, and Optimism and a medium positive effect of higher training levels on Impulse Control. Medium effects for residency type were found for Stress Tolerance, Flexibility, and Stress Management, with higher scores for residencies with heavy inpatient loads. Medium effects for residency type were found on Flexibility scores, with higher scores for residents in disciplines with a perceived high stress level. We found that baseline EI scores of 4th-year veterinary students, interns, and residents at a teaching hospital were similar to, but uniformly lower than, those of other professionals and did not increase with training level. These results may be used to build on strengths and address weaknesses associated with EI of students and house officers at this institution.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".