Inferential confusion, obsessive beliefs and obsessive–compulsive symptoms: a multidimensional investigation of cognitive domains
Bibliographic record
Abstract
Generally, research into the relationship between cognitive domains and obsessive-compulsive symptoms involves the use of scales that are highly intercorrelated with each other. The current study investigates the relationship between cognitive constructs and obsessive-compulsive symptoms using the item set of the Obsessive Beliefs Questionnaire and the Inferential Confusion Questionnaire. In order to create constructs that would not be excessively correlated with each other, factor scores were used to investigate the relationship between cognitive domains and obsessive-compulsive symptoms. Factor analysis followed by oblique rotation resulted in four moderately correlated cognitive constructs (importance/control of thoughts, inferential confusion/threat estimation, perfectionism/certainty and responsibility for preventing harm). With the exception of responsibility for preventing harm, the cognitive constructs under investigation were quite strongly related to obsessive-compulsive symptoms. In particular, hierarchical regression revealed the construct inferential confusion/threat estimation to be a global and strong predictor of obsessive-compulsive symptoms, followed by the constructs of perfectionism/certainty and the construct importance/control. Responsibility for preventing harm acted to be a negative predictor of obsessive-compulsive symptoms. It is concluded that the construct of inferential confusion acts as a more powerful predictor of obsessive-compulsive symptoms than any specific obsessive belief
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".