Emotional intelligence, empathy and alexithymia: a cross-sectional survey on emotional competence in a group of nursing students.
Bibliographic record
Abstract
BACKGROUND: Emotional intelligence (EI) is the ability to recognize and manage one's own and others' emotions, empathy is the ability to understand how others feel, whereas alexithymia represents the difficulty in feeling and verbally expressing emotions. Emotional competences are important requirements for positive outcomes in nursing profession. THE AIM OF THE STUDY: To analyze EI, empathy and alexithymia in nursing students. METHODS: We conducted a cross-sectional survey in a sample of 237 students (53 males, 184 females), attending both the 1st and 3rd year of the University Nursing Course in Modena. We administered three Italian validated scales: Schutte Self-Report Emotional Intelligence Test (SSEIT), Jefferson Scale of Empathy - Health Professions Student (JSE-HPS), Toronto Alexithymia Scale (TAS-20). Data were statistically analyzed. RESULTS: Statistically significant differences were found between the 1st and 3rd year students at SSEIT (t=-0.6, p=0.52), JSE-HPS (t=-3.2, p=0.0016) and TAS-20 scores (t=-3.54, p=0.0005). Among 3rd year students, females obtained significantly different scores from those of males at SSEIT (t=2.8, p=0.006). All three scales reported a Cronbach's alpha >0.80. SSEIT correlated positively with JSE-HPS (Spearman's rho=0.15, p=0.02) and negatively with TAS-20 (Spearman's rho=-0.18, p=0.006). CONCLUSIONS: Our study highlighted a good level of emotional skills among students at the beginning of nursing training, further increased by the last year of the course, suggesting that emotional competences can be learned, and confirmed that empathy, but not alexithymia, is a dimension of EI.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".