Comparison of Cognitive Emotion Regulation Strategies and Understanding Emotions in Obsessive Compulsive, Generalized Anxiety, and Social Anxiety Disorders
Bibliographic record
Abstract
The purpose of the present study was to compare the cognitive emotion regulation strategies and understanding emotions in obsessive compulsive disorder, generalized anxiety disorder, social anxiety disorder, and normal people. The research method was causal-comparative. The statistical population were all male and female patients with the disorders mentioned, in private psychiatric centers in Shiraz (2012 and 2013). 300 patients (100 patients in each disorder group) were selected through available sampling method. 100 people from those who accompanied patients considered as the normal participants. All participants completed the Cognitive Emotion Regulation Questionnaire (Garnefski et al., 2001), the 20-item Toronto Alexithymia Scale (Bagby et al., 1994), and General Health Questionnaire (Goldberg & Hillier, 1979). The results of multivariate analysis of variance and Tukey post-hoc test showed that the three patient groups reported more maladaptive cognitive emotional regulation strategies, less adaptive strategies, and lower understanding of emotions than the normal group (P<0.001). There was a significant difference among the three patient groups in using adaptive strategies and low understanding of emotions (P<0.001). In general, cognitive emotion regulation strategies and understanding emotions appear differently in these disorders.
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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".