The relationship between alexithymia, cognitive avoidance, and distress tolerance with the dimensions of obsessive-compulsive disorder symptoms
Bibliographic record
Abstract
The purpose of this study was to investigate the relationship between alexithymia, cognitive avoidance, and distress tolerance with obsessive-compulsive symptoms. A total of 150 students (105 girls and 45 boys) from the Ferdowsi University of Mashhad participated in this research. The participants were asked to complete the Persian version of the 20-Item Toronto Alexithymia Scale (TAS -20), the Cognitive Avoidance Scale (CAQ), the Distress Tolerance Questionnaire (DTS), Maudsley Obsessive-Compulsive Inventory (MOCI), and demographic data questionnaire. The data were analyzed using Pearson correlation and stepwise regression. The highest correlation relationships were between Obsessive-Compulsive with a total score of Alexithymia (r=0.43), the total score of Cognitive Avoidance Questionnaire (r=0.39), the total score of Distress Tolerance Scale (r=-0.43), Checking sub-scale of Obsessive-Compulsive Scale (r=0.71), Cleaning sub-scale of Obsessive-Compulsive Scale (r=0.75), Slowness sub-scale of Obsessive-Compulsive Scale (r=0.48) and Doubting sub-scale of Obsessive-Compulsive Scale (r=0.68). This study showed that the subscales of cleanliness, revision, hesitation, and slowness of obsessive-compulsive disorder and emotional dyslexia, distress tolerance, and cognitive avoidance have the greatest contribution in predicting obsessive-compulsive disorder. The results from the present study highlight the need to pay attention to these variables in the research and treatment of obsessive-compulsive disorder.
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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) | 0.002 | 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".