Are There Interactions Among Dysfunctional Beliefs in Obsessive Compulsive Disorder?
Bibliographic record
Abstract
Contemporary cognitive models of obsessive-compulsive disorder emphasize the importance of various types of dysfunctional beliefs, such as beliefs about inflated responsibility, perfectionism and the importance of controlling one's thoughts. These beliefs have been conceptualized as main effects, each influencing obsessive-compulsive symptoms independent of the contributions of other beliefs. It is not known whether beliefs interact with one another in their influence on obsessive-compulsive symptoms. To investigate this issue, data from 248 obsessive-compulsive disorder patients were analyzed. Dependent variables were the factor scores on the 4 Padua Inventory subscales. Predictor variables were the factor scores from the 3 factors (inflated responsibility, perfectionism and controlling one's thoughts) of the Obsessive Beliefs Questionnaire and their 2- and 3-way interactions. Regression analyses revealed significant main effects; in almost all analyses one or more of inflated responsibility, perfectionism, and controlling one's thoughts factors predicted scores on the Padua factors even after controlling for general distress. There was no evidence that beliefs interact in their effects on obsessive-compulsive symptoms, thereby providing a relatively unusual instance in which a simpler explanation (main effects only) is just as powerful as a more complex model.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".