Emotional Skills and Nursing Training: A Study on Italian Stu-Dents and a TRI-COM Based Educational Model
Bibliographic record
Abstract
Background/Objectives: Emotional competence is increasingly recognized as a core component of professional nursing. However, emotional education remains largely unstructured within nursing curricula. This study aimed to (1) propose a conceptual framework for defining and operation-alizing "emotion" in nursing education using the TRI-COM model, and (2) investigate the multi-dimensional structure of emotional competence in a sample of Italian nursing students. Methods: A cross-sectional study was conducted with 233 nursing students from various Italian universi-ties. Participants completed the Toronto Alexithymia Scale (TAS-20) and the Jefferson Scale of Empathy (JSE). Data were analyzed using descriptive statistics, ANOVA, t-tests, and Pearson's correlations. Results: The sample showed moderate levels of alexithymia, with 49.6% scoring above the clinical cut-off. The subscale “Externally Oriented Thinking” showed the highest prevalence (56%). Empathy scores indicated adequate perspective-taking (M = 60.95) but lower scores in compassionate care (M = 18.92). No significant gender differences were observed in overall alexithymia or empathy, though women scored slightly higher in emotional description. ANOVA results revealed no significant differences in emotional competence across years of study, though DIF scores (difficulty identifying feelings) approached significance (p = .053). Correlational analyses showed inverse relationships between age and alexithymia, and between alexithymia and empathy dimensions. Conclusions: Findings suggest an emotional competence gap among nursing students, with potential roots in learned helplessness and insufficient training. The TRI-COM model offers a pedagogically actionable structure to support the devel-opment of emotional literacy through cognitive, physiological, and motivational regulation. Structured emotional education should be integrated into nursing curricula to promote clinical efficacy and student well-being.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".