Dysregulation of positive emotions across community, clinical and forensic samples using the Italian version of the difficulties in emotion regulation scale -positive (DERS-positive)
Bibliographic record
Abstract
Literature mainly focuses on the role of dysregulation of negative emotions whereas the topic of dysregulation of positive emotions has been widely neglected. This study aims to explore levels of dysregulation of positive emotions across community, clinical and forensic samples. The Difficulties in Emotion Regulation Scale – Positive (DERS-Positive) was administered to a total sample of 1044 participants, divided in 497 community participants (Mage = 39.18 years), 464 forensic individuals (Mage = 39.36 years), and 83 individuals diagnosed with Bipolar disorder (Mage = 47.26 years). The Difficulties in Emotion Regulation Scale (DERS) and the Toronto Alexithymia Scale (TAS-20) were administered to a subsample of community participants (n = 105). Confirmatory Factor Analyses supported the three-factor structure of the DERS- Positive in both clinical and non-clinical samples. Correlations between DERS-Positive, DERS and TAS-20 scores indicated a good construct validity of the DERS- Positive. We found that the three groups significantly differed from each other on DERS-Positive total scores. Individuals with Bipolar disorders showed higher levels of dysregulation of positive emotions compared to both offenders and community participants. Offenders scored higher on the DERS- Positive compared to community participants. Dysregulation of positive emotions is an often overlooked yet relevant construct that may account for maladaptive behavior and psychopathology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".