Hierarchical Structure of Dysfunctional Beliefs in Obsessive‐Compulsive Disorder
Bibliographic record
Abstract
The Obsessive Beliefs Questionnaire was developed as a comprehensive measure of dysfunctional beliefs, which cognitive models consider to be etiologically related to obsessive-compulsive disorder. Obsessive Beliefs Questionnaire subscales tend to be highly correlated, which raises the question of whether obsessive-compulsive-related beliefs are hierarchically structured, consisting of lower-order factors loading on 1 or more higher-order factors. To investigate the nature and relative importance of these factors, a hierarchical factor analysis was conducted (n = 202 obsessive-compulsive disorder patients), using a Schmid-Leiman transformation. Results indicated a higher-order (general factor) and 3 lower-order factors: (i) responsibility and overestimation of threat, (ii) perfectionism and intolerance of uncertainty and (iii) importance and control of thoughts. The high-order factor accounted for more variance in Obsessive Beliefs Questionnaire scores (22%) than did the lower-order factors (6-7%), thereby underscoring the importance of the higher-order factor. Despite the importance of the higher-order factor, the lower-order factors significantly predicted unique variance in measures of obsessive-compulsive symptoms, including severity ratings of compulsions. These finding suggest that cognitive models of obsessive-compulsive disorder should take into consideration the hierarchic structure of obsessive-compulsive-related beliefs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".