Inferential Confusion and Obsessive Beliefs in Obsessive‐Compulsive Disorder
Bibliographic record
Abstract
The goal of the present study was to investigate whether inferential confusion could account for the relationships between obsessional beliefs and obsessive-compulsive disorder (OCD). The Inferential Confusion Questionnaire and the Obsessive Beliefs Questionnaire were administered to a sample of 85 participants diagnosed with OCD. Results showed that the relationship between obsessive beliefs and obsessive-compulsive symptoms decreased considerably when controlling for inferential confusion. Conversely, the relationship between inferential confusion and obsessive-compulsive symptoms was not substantially affected when controlling for obsessive beliefs. Since inferential confusion has an overlap with overestimation of threat, a competing hypothesis for the results was investigated. Results indicated that inferential confusion was factorially distinct from overestimation of threat, and that the independent construct of inferential confusion remains significantly related to obsessive-compulsive symptoms when controlling for anxious mood. These results are consistent with the claim that inferential confusion may be a more critical factor in accounting for OCD symptoms than are obsessive beliefs and appraisals.
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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 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".