The Impact of Personality on Symptom Expression in Obsessive-Compulsive Disorder
Bibliographic record
Abstract
Previous research conducted on the five-factor model of personality (FFM) in obsessive-compulsive disorder (OCD) has demonstrated that community and clinical participants score significantly higher than controls on the domains and facets of neuroticism and extraversion and selective facets of agreeableness and conscientiousness. However, studies have yet to examine the extent to which personality traits, as assessed by the FFM, are associated with the specific symptoms of OCD. The purpose of this study was to examine further the personality predictors of obsessive-compulsive symptoms in clinical participants using the facets of the FFM. Patients with a DSM-IV diagnosis of OCD (N = 56) completed the Revised NEO Personality Inventory, the Yale Brown Obsession Compulsion Scale, and the Beck Depression Inventory. Lower scores on openness to ideas were uniquely associated with greater obsession severity, whereas lower openness to actions was uniquely associated with greater compulsive severity. In contrast with past research that has emphasized the association between neuroticism and extraversion and dimensionally rated obsessive-compulsive symptoms, this study demonstrates the specific associations between selective facet traits of openness and clinical obsessions and compulsions. Whereas tendencies toward negative affectivity may confer a nonspecific vulnerability to the development of OCD, facets of openness may impact on the particular expression and severity of obsessive-compulsive symptoms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".