The Association of Self-Assessed Emotional Intelligence with Academic Achievement and General Health among Students of Medical Sciences
Bibliographic record
Abstract
INTRODUCTION & AIM: Emotional intelligence is one of the most important leading factors influencing different aspects of human life. It leads individuals to percept their feelings for appropriate decision making and fields for future accomplishments. The aim of this study was to Investigation of the associations of self-assessed EI with academic achievement and general health among medical students. MATERIALS & METHODS: This cross-sectional study was conducted on 426 students of Zahedan University of Medical Sciences from October 2014 to May 2015. Random sampling method was used. Sibria Shring standard emotional intelligence questionnaire and Goldberg's standard general health questionnaire (GHQ-28) were used for data collecting. Data analysis was through descriptive statistics (mean, standard deviation) and inferential statistics (t-test, ANOVA and Pearson correlation coefficient) by SPSS v.21. The tests significant level was considered 0.05. FINDINGS: The average total score of emotional intelligence in males (102.23±1.67) was better in comparison with females (98.54±2.23). There was not any significant difference in total mean scores of students of different fields of Study (P=0.211). According to ANOVA test, it has not observed any significant difference between scores of emotional intelligence scales of students from different domains of study. The results of Pearson correlation test confirmed a positive significant correlation between emotional intelligence, academic achievement and general health. CONCLUSION: According to the results of this study that has shown a significant relationship between emotional intelligence, general health and academic achievement, it is needed to hold some workshops and classes for emotional intelligence improvement.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".