Correlates of emotional intelligence: Results from a multi-institutional study among undergraduate medical students
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Bibliographic record
Abstract
BACKGROUND: Emotional Intelligence (EI) is the ability to deal with your own and others emotions. Medical students are inducted into medical schools on the basis of their academic achievement. Professionally, however, their success rate is variable and may depend on their interpersonal relationships. EI is thought to be significant in achieving good interpersonal relationships and success in life and career. Therefore, it is important to measure EI and understand its correlates in an undergraduate medical student population. AIM: The objective of study was to investigate the relationship between the EI of medical students and their academic achievement (based on cumulative grade point average [CGPA]), age, gender and year of study. METHODS: A cross-sectional survey design was used. The SSREIS and demographic survey were administered in the three medical schools in Saudi Arabia from April to May 2012. RESULTS: The response rate was 30%. For the Optimism subscale, the mean score was M = 3.79, SD ± 0.54 (α = 0.82), for Awareness-of-emotion subscale M = 3.94, SD ± 0.57 (α = 0.72) and for Use-of-emotion subscale M = 3.92, SD ± 0.54 (α = 0.63). Multiple regression showed a significant positive correlation between CGPA and the EI of medical students (r = 0.246, p = 0.000) on the Optimism subscale. No correlation was seen between CGPA and Awareness of Emotions and Use of Emotions subscales. No relationship was seen for the other independent variables. CONCLUSION: The current study demonstrates that CGPA is the only significant predictor, indicating that Optimism tends to be higher for students with a higher CPGA. None of the other independent variables (age, year of study, gender) showed a significant relationship.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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 it