On the Effectiveness of Emotion Regulation Training in Anger Management and Emotional Regulation Difficulties in Adolescents
Bibliographic record
Abstract
Introduction: Considering the high prevalence of anger problems in adolescence and the importance of emotion regulation in this age period, this study aims to assess the effectiveness of emotional regulation training in anger management and emotion regulation difficulties in adolescents by means of body postures, breathing pattern, and facial expressions.Method: A quasi-experimental research design along with pretest-posttest and control group was employed for the conduct of this study. For this purpose, the number of 50 adolescents was selected via purposive sampling through screening based on inclusion and exclusion criteria. These participants were randomly assigned to experimental and control groups and the experimental group then received 8 sessions of emotion regulation training. In this period, the control group received the treatment unrelated to emotion regulation training. Both groups were assessed in terms of Difficulties in Emotion Regulation Scale (DERS) and State-Trait Anger Expression Inventory-2 (STAXI-2) before and after the treatment as well as in the three-month follow-up. The collected data were analyzed by descriptive and inferential statistics, including covariance analysis.Results: Data analysis showed that emotion regulation training has a significant effect on anger management and components emotion regulation difficulties in adolescents.Conclusion: As per the results of this study, it is recommended that emotion regulation training be extensively conducted in the form of educational and training programs for adolescents, especially in schools.
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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 | 0.001 |
| 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".