Emotion Regulation Training on Irritability, Alexithymia, and Interpersonal Problems of Adolescents With Disruptive Mood Dysregulation Disorder
Bibliographic record
Abstract
Background: Difficulty regulating emotion has been identified as a trans-diagnostic factor common to various psychiatric diagnoses and behavior problems. This study aims to implement emotion regulation training techniques on adolescents with a disruptive mood disorder, emphasizing the irritability, alexithymia, and interpersonal issues in adolescents with disruptive mood dysregulation disorder. Methods: This quasi-experimental research used a pre-test and post-test design on 30 disruptive mood disorders in Tehran City, Iran, from 2022 to 2023. The participants were selected via simple purposive sampling. They were randomly assigned to two 15-member groups (experimental and control). The intervention group received self-regulation training over two months through eight 90-min group therapy sessions, whereas the control group received no treatment. The data were collected using the Barratt impulsiveness scale (BIS-11), Toronto alexithymia scale (TAS-20), and the inventory of interpersonal problems short-version. The data were analyzed by the SPSS software, version 25, and the multivariate analysis of covariance. Results: As indicated by the results, a significant difference was detected between the groups in terms of irritability (F=26.45, P=0.001, η=0.695), alexithymia (F=38.91, P=0.001, η=0.781), and interpersonal problems (F=31.27, P=0.001, η=0.734). Moreover, according to the alexithymia’s largest effect size (0.781), emotion regulation training had more effect on alexithymia. Conclusion: Based on the results, emotion regulation training can be implemented effectively in clinics and psychological treatment centers. Also, because of using emotion regulation training, it is possible to improve these people’s psychological characteristics and social relations. It is also suggested that relevant organizations train specialists and school counselors accordingly.
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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 | 0.000 |
| 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".