Biopsychosocial etiology of obsessions and compulsions: An integrated behavioral–genetic and cognitive–behavioral analysis.
Bibliographic record
Abstract
Accumulating evidence suggests that particular kinds of dysfunctional beliefs contribute to obsessive-compulsive (OC) symptoms. Three domains of beliefs have been identified: (a) perfectionism and intolerance of uncertainty, (b) overimportance of thoughts and the need to control thoughts, and (c) inflated responsibility and overestimation of threat. These beliefs and OC symptoms are both heritable. Although it is widely acknowledged that OC symptoms probably have a complex biopsychosocial etiology, to our knowledge there has been no previous attempt to integrate dysfunctional beliefs and genetic factors into a unified, empirically supported model. The present study was an initial step in that direction. A community sample of monozygotic and dizygotic twins (N = 307 pairs) completed measures of dysfunctional beliefs and OC symptoms. Structural equation modeling was used to compare 3 models: (a) the belief causation model, in which genetic and environmental factors influence beliefs and OC symptoms, and beliefs also influence symptoms; (b) the symptom causation model, which is the same as (a) except that symptoms cause beliefs; and (c) the belief coeffect model, in which beliefs and OC symptoms are the product of common genetic and environmental factors, and beliefs have no causal influence on symptoms. The belief causation model was the best fitting model. Beliefs accounted for a mean of 18% of phenotypic variance in OC symptoms. Genetic and environmental factors, respectively, accounted for an additional 36% and 47% of phenotypic variance. The results suggest that further biopsychosocial investigations may be fruitful for unraveling the etiology of obsessions and compulsions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".