Gender Differences in Alexithymia, Emotion Regulation, and Impulsivity in Young Individuals with Mood Disorders
Bibliographic record
Abstract
Background/Objectives: Alexithymia, emotion regulation, and impulsivity are key factors in youths with mood disorders. However, gender differences within these dimensions remain insufficiently studied in this population. This study seeks to explore these dimensions in a sample of adolescents and young adults with mood disorders, aiming to identify gender-specific characteristics with important clinical implications. Methods: We assessed 115 outpatients aged 13 to 25 years with a DSM-5 diagnosis of mood disorder. The evaluation included the Toronto Alexithymia Scale (TAS-20), the Difficulties in Emotion Regulation Scale (DERS), and the UPPS-P Impulsive Behavior Scale. The associations with suicidal ideation were tested using two different multivariate models. Results were controlled for age and intelligence measures. Results: The first model (Wilks’ Lambda = 0.720, p < 0.001) revealed significantly higher scores in women than men for TAS-20 (p < 0.001), DERS (p < 0.001), and the UPPS-P subscales “Lack of Premeditation” (p = 0.004) and “Lack of Perseverance” (p = 0.001). Regression analyses confirmed gender as a significant predictor of these variables, also controlling for age and intelligence. Furthermore, intelligence measure influenced Lack of Premeditation and age influenced Lack of Perseverance. Conclusions: Women with mood disorders exhibit greater alexithymia, emotional dysregulation, and impulsivity, particularly in difficulties with planning and task persistence. These findings highlight the need for gender-sensitive interventions that address emotional awareness and impulse control to improve clinical outcomes.
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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 | 0.001 | 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".