The Role of Behavioral Activation-Inhibition, Impulsivity and Alexithymia in Discriminating Students with Symptoms of Obsessive-Compulsive and Paranoid Personality Disorders from Normal Controls
Bibliographic record
Abstract
Background and Aims: The purpose of the study was to examine the role of behavioral activation-inhibition, impulsivityand alexithymia in discriminating students with symptoms of obsessive-compulsive and paranoid personality disorders from normal controls. Methods: The studied sample includes all the students in University of Mohaghegh Ardabili, Iran, in 2011 (n = 8344). At the first stage, to identify the students with obsessive-compulsive and paranoid personality disorders, 368 students were selected through simple random sampling method and the Millon Clinical Multiaxial Inventory-III was completed for them. In the second stage, 25 students were selected for each group and the Gray-Wilson Personality Questionnaire, Barratt Impulsiveness Scale and the Toronto Alexithymia Scale were completed by them. Data were analyzed using descriptive statistics and discriminant analysis. Results: Behavioral activation-inhibition, impulsivity and alexithymia played significant role in discriminating students with symptoms of obsessive-compulsive and paranoid personality disorders and had the potential to predict the changes concerned with obsessive-compulsive and paranoid personality disorders. Conclusion: It seems that attention to and evaluation of the roles of behavioral activation-inhibition, impulsivity and alexithymia as effective factors in obsessive-compulsive and paranoid personality disorders is necessary.
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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 | 0.002 |
| 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 | 0.001 | 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".