Predictors and Consequences of Suppressing Obsessional Thoughts
Bibliographic record
Abstract
Abstract Cognitive-behavioral models of obsessive compulsive disorder (OCD) assert that negative appraisals of obsessional thoughts lead to distress over the thoughts and drive ameliorative actions such as thought suppression and compulsions. These responses in turn play a role in the persistence of the disorder. However, past research has not examined (a) what factors lead individuals to suppress obsessional thoughts; (b) whether certain predictors and consequences relate to suppression uniquely or can be explained by general factors such as negative mood and neuroticism; or (c) individuals' natural active suppression of obsessions. The current study addresses these limitations by examining the roles of natural suppression and distress over thought intrusions in the thought-appraisal/OC symptoms relationship while controlling for general factors. Ninety-one nonclinical participants completed a variety of measures assessing theoretically relevant constructs. After their obsessional thought was primed, they recorded their thoughts for 6 minutes and then rated their suppression effort. Four hours later, longer-term outcomes were assessed. Path analyses supported most components of cognitive-behavioral models.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".