Correlates of emotional intelligence: Results from a multi-institutional study among undergraduate medical students
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".