Relationships between family dysfunction, alexithymia, and low emotional intelligence among early adolescents in Tehran, Iran
Bibliographic record
Abstract
Considering the potential influences on emotional intelligence are important andbeneficial.If several factors are found to influence emotional intelligence, then individuals will able to find ways to enhance emotional intelligence and following lifelong success. In Iran, however, the study on relationship between family dysfunctioning, alexithymia with low emotional intelligence has received very little attention. Thus, the current research is essential and necessary. The main purpose of this study was to determine the relationships between family dysfunctioning,alexithymia and low emotional intelligence among early adolescents in Tehran-Iran.There were a total of 234 early adolescents (115 boys and 119 girls in Grades 2 and 3 of Guidance Schools of Tehran) participating in this study. They were identified using Multi-Stage Cluster sampling. Data were collected using self-administered questionnaire, namely, Background Characteristics questionnaire, Schutte’s (1998)Emotional Intelligence Scale, Rieffe’s Children’s Alexithymia Scale (2006), which are consistent with the original adult questionnaire for alexithymia (TAS-20), and Family Assessment Device (FAD), based on McMaster’s model.The findings of the present study highlighted the importance of early adolescent’s background in enhancing emotional intelligence. However, the contribution of family dysfunctioning in low emotional intelligence is indirect through alexithymia.The nature of the relationships between family dysfunctioning, alexithymia (as a mediator)and low emotional intelligence implied that emotional intelligence of early adolescents could be improved if families learn how to identify, express and manage their emotions since they can model healthy identification, expression and management of emotions in their early adolescents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".