Examining the Effectiveness of Emotional Intelligence Training on Alexithymia Components in Students
Bibliographic record
Abstract
Objective: Emotional intelligence is the most crucial factor in decision-making and the selection of future goals, as well as the substitution of goals in various situations and circumstances. The purpose of this article was to determine the effectiveness of emotional intelligence training on alexithymia components in students. Methods and Materials: This study was applied, quantitative, and of a quasi-experimental pre-test - post-test design with a control group and training. The population consisted of 30 students suffering from alexithymia, selected from 5 treatment centers, with 15 students allocated to the control group and 15 to the training group. The data collection tool in this research was the Toronto Alexithymia Scale (TAS-20), and Goleman's (1996) emotional intelligence training protocol was used for the experimental group. Data were analyzed using SPSS software and multivariate analysis of covariance method. Findings: The F-value in the univariate analysis of covariance for the subscale of identifying emotions (F=13.266, P=0.000), for the subscale of describing emotions (F=19.917, P=0.000), and for the subscale of external-oriented thinking (F=11.108, P=0.000) were significant. These findings indicate that there is a significant difference between the emotional intelligence training group and the control group in the dependent variables (alexithymia components). Conclusion: It can be concluded that the use of emotional intelligence training is beneficial for improving alexithymia in students with learning disabilities.
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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) | 0.000 | 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) | 0.001 | 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".