Psychometric properties of the French version of a scale measuring perceived emotional intelligence : the Trait Meta-Mood Scale (TMMS)
Bibliographic record
Abstract
OBJECTIVE: The Trait Meta-Mood Scale (TMMS), a 30-item self-assessment questionnaire, has been developed to measure perceived emotional intelligence (EI) level in 3 dimensions: Attention, Clarity and Repair. This study aimed to explore the psychometric properties of the French version of this instrument. METHOD: The instrument factor structure, normality, internal consistency, stability and concurrent validity were assessed in a sample of 824 young adults (456 female). Besides TMMS, participants completed self-assessment questionnaires for affectivity (Shortened Beck Depression Inventory, State and Trait Anxiety Inventory, Positive and Negative emotion scale), alexithymia (Bermond-Vorst Alexithymia Questionnaire-B) and interpersonal functioning (Empathy Quotient). Discriminant validity was tested in 64 female patients with anorexia nervosa, identified in literature as having difficulties with introspection, expression and emotional regulation. RESULTS: Confirmatory factor analysis results replicate the 3-factor structure. Internal consistency and reliability indices are adequate. Direction and degree of correlation coefficients between TMMS dimensions and other questionnaires support the instrument concurrent validity. TMMS allows to highlight differences in perceived EI levels between men and women (Attention: p < 0.001 ; Clarity: p < 0.05) as well as between patients with anorexia nervosa and control subjects (p < 0.001 for all 3 dimensions). CONCLUSION: This first validation study shows satisfying psychometric properties for TMMS French version.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".