Psychoeducation Reduces Alexithymia and Modulates Anger Expression in a School Setting
Bibliographic record
Abstract
Bullying and violence are relevant issues in school settings and negatively impact students' well-being and mental health. Psychoeducation and anti-bullying programs may prevent violence among students by addressing emotional expression and regulation, alexithymia, and anger. We describe the impact of a psychoeducational intervention delivered to 90 male and 101 female school youths (N = 191), aged 12-14 years old, and aimed to improve their emotional recognition and regulation, as well as to reduce alexithymia in order to prevent aggression and bullying episodes. A psychological assessment has been performed before (T0) and after (T1) the intervention including levels of alexithymia, measured with the Toronto Alexithymia Scale-20 (TAS-20), the State-Trait Anger Expression Inventory (STAXI), Empathy Quotient (EQ), and the Emotion Regulation Questionnaire (ERQ). Females have shown higher levels of alexithymia at baseline whereas other characteristics (anger, empathy quotient and emotional regulation) did not differ among sex groups. The psychoeducational program significantly increased the empathy quotient (+10.2%), the emotional regulation reappraisal (+20.3%), and the assertive anger expression (+10.9%); alexithymia significantly decreased after the intervention in all the samples (-14.4%), above all among students scoring ≥61 at TAS-20 (-48.2%). Limitations include a small sample from a single school setting, the lack of a control group without psychoeducation, and an assessment based on self-reported measures. We may conclude that psychoeducation has significantly reduced levels of alexithymia and improved empathy and emotional regulation among adolescents.
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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) | 0.001 | 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".