Emotional Intelligence, Alexithymia, and Interpersonal Problems
Bibliographic record
Abstract
The aim of this study was to examine the relations of emotional intelligence with alexithymia and interpersonal problems in a sample of students. A correlational analysis was performed to assess the kind of association that exists between emotional intelligence, alexithymia and interpersonal problems. Three hundred and fifty seven students (147 boys, 210 girls) from the University of Tehran were included in this study. All participants were asked to complete Emotional Intelligence Scale (EIS), Farsi version of the Toronto Alexithymia Scale (FTAS-20), and Inventory of Interpersonal Problems (IIP). Analysis of the data involved both descriptive and inferential statistics including means, standard deviations, multivariate analysis of variance, Pearson's correlation coefficients and regression analysis. The results revealed that emotional intelligence was negatively associated with both alexithymia and different aspects of interpersonal problems including assertiveness, sociability, intimacy, and responsibility. Results of regression analysis revealed that emotional intelligence can predict alexithymia and interpersonal problems regarding assertiveness, sociability, intimacy, and responsibility. It can be concluded that emotional intelligence is associated with alexithymia and interpersonal problems. Results and implications are discussed.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".