Emotion Regulation Strategies in Depression and Somatization Disorder
Bibliographic record
Abstract
Scant research has investigated emotion regulation strategies in somatization disorder, despite its high comorbidity with depression and the growing interest in this topic in depression. The present study investigated emotion regulation strategies in patients with major depression and somatization disorder using clinical samples to examine common vulnerability factors and to provide evidence for difficulties in emotion regulation as transdiagnostic factors in these disorders. Patients with major depressive disorder ( n = 30) and patients with somatization disorder ( n = 30) completed measures of putatively adaptive and maladaptive emotion regulation strategy use. Patients with somatization disorder showed higher scores on measures of regulatory strategies, as measured by the sum of adaptive strategies in the Cognitive Emotion Regulation Questionnaire as well as the following subscales: positive refocusing, positive reappraisal, and refocusing on a plan. After controlling for levels of current depression, the significant effects remained for positive refocusing. Depression symptom severity was significantly and negatively correlated with most adaptive strategies and positively correlated with most maladaptive strategies. The current results provide preliminary data for a similar pattern of adaptive and maladaptive emotion regulation strategies usage in these two disorders. The results also contribute to theories of psychopathology and our understanding of critical cognitive and emotional processes.
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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 | 0.002 |
| 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) | 0.001 | 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".