Perceived emotional intelligence in nursing: psychometric properties of the <scp>T</scp>rait <scp>M</scp>eta‐<scp>M</scp>ood <scp>S</scp>cale
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To examine the psychometric properties of the Trait Meta-Mood Scale in the nursing context and to determine the relationships between emotional intelligence, self-esteem, alexithymia and death anxiety. BACKGROUND: The Trait Meta-Mood Scale is one of the most widely used self-report measures for assessing perceived emotional intelligence. However, in the nursing context, no extensive analysis has been conducted to examine its psychometric properties. DESIGN: Cross-sectional and observational study. METHODS: A total of 1417 subjects participated in the study (1208 nursing students and 209 hospital nurses). The Trait Meta-Mood Scale, the Toronto Alexithymia Scale, the Rosenberg Self-Esteem Scale and the Death Anxiety Inventory were all applied to half of the sample (n = 707). A confirmatory factor analysis was carried out, and statistical analyses examined the internal consistency and test-retest reliability of the Trait Meta-Mood Scale, as well as its relationship with relevant variables. RESULTS: Confirmatory factor analysis confirmed the three dimensions of the original scale (Attention, Clarity and Repair). The instrument showed adequate internal consistency and temporal stability. Correlational results indicated that nurses with high scores on emotional Attention experience more death anxiety, report greater difficulties identifying feelings and have less self-esteem. By contrast, nurses with high levels of emotional Clarity and Repair showed less death anxiety and higher levels of self-esteem. CONCLUSIONS: The Trait Meta-Mood Scale is an effective, valid and reliable tool for measuring perceived emotional intelligence in the nursing context. Training programmes should seek to promote emotional abilities among nurses. RELEVANCE TO CLINICAL PRACTICE: Use of the Trait Meta-Mood Scale in the nursing context would provide information about nurses' perceived abilities to interpret and manage emotions when interacting with patients.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".