Assessing Interpersonal and Intrapersonal Emotional Insights in Undergraduate Medical Students
Bibliographic record
Abstract
Emotional intelligence (EI) plays a vital role in professional competence and psychological well-being, especially in healthcare. It significantly impacts how effective practitioners are and the outcomes for patients. In medical education, nurturing emotional intelligence is crucial because clinical practice often involves intense emotional experiences. Objective: To evaluate emotional understanding at both the interpersonal and intrapersonal levels among medical students. Methods: A Descriptive cross-sectional study was conducted at Sialkot Medical College, from August 23, 2023, and culminating on November 23, 2023 of three months’ duration after taking IRB from Sialkot Medical College, Sialkot IRB no. (MRC/IRB/23019). The selection of participants was conducted utilizing a convenience sampling approach. The study included students across all academic years of the MBBS program, with the exclusion criteria being individuals diagnosed with anxiety or depression. Data were collected via a Google Forms questionnaire and analyzed using SPSS Version 23.0. Results: The study encompassed a total of 298 medical students, with an average age of 20.4 ± 1.77 years. The demographic breakdown revealed 143 (48%) male participants and 155 (52%) female participants. The findings underscored a prevalent understanding among students regarding their emotions and the significance of emotional awareness in their daily lives. Moreover, the research identified obstacles related to emotional expression and heightened sensitivity to external stimuli. Conclusions: This study concluded that brings substantial diversities in emotional and social-emotional acumen within the medical student cohort, underscoring the necessity for augmented emotional intelligence training in medical curricula to fortify self-awareness and interpersonal efficacy.
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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 | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".