Effectiveness of Emotional Intelligence Training on Alexithymia of Male Students
Bibliographic record
Abstract
Background: Emotions are part of the human nature and are considered as one the necessities of human life. However, if left uncontrolled or unregulated, they will create problems for the individual and others. Objectives: The aim of the present study was to investigate the effect of emotional intelligence training on alexithymia of male students with high levels of alexithymia. Materials and Methods: The statistical population consisted of undergraduate male students enrolled at Shahid Chamran University of Ahvaz between 2012 and 2013. Samples were selected via two stages by using the random cluster multistage method. The first stage involved distributing and collecting 623 measures of alexithymia (TAS-20) in the form of a multi-stage cluster between students and also selecting 40 individuals from those with scores of a higher standard deviation than the average in this scale. The second stage consisted of selecting 30 individuals who possessed the criteria for entering the groups. The test group after eight training sessions (two times a week) and the control group without any intervention simultaneously completed the post-test. For data analysis, analysis of univariate covariance (ANCOVA) was used. Results: Statistical analysis showed that emotional intelligence training affected male students with alexithymia (Eta2 = 0.77 and P < 0.0001 and F = 92.27) and the mean score of emotional alexithymia related to the experimental group reduced from 67.80 to 51.60. Conclusions: According to these findings, emotional intelligence can be taught in order to reduce the level of students’ alexithymia and improve emotional problems in individuals with high alexithymia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".