Schizotypal, Dissociative, and Imaginative Processes in a Clinical OCD Sample
Bibliographic record
Abstract
OBJECTIVE: Previous research in a nonclinical sample has suggested that schizotypal, dissociative, and imaginative processes may play a role in obsessive-compulsive disorder (OCD) symptoms (Aardema & Wu, ). The present study aims to extend these findings in a clinical sample. METHOD: N = 75 adults (mean age = 37.99; 61.3% female), meeting the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision, diagnostic criteria for OCD completed a battery of self-report questionnaires measuring schizotypal, dissociative, and imaginative processes. RESULTS: Hierarchical regression analyses revealed inferential confusion and dissociation to be the strongest predictors of OCD symptoms, replicating and extending the findings by Aardema and Wu (). CONCLUSION: Results support the notion that inferential confusion and dissociation are important variables to consider in understanding symptoms of OCD independently from obsessive beliefs and negative mood states.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".