Poor insight in obsessive-compulsive disorder: examining the role of cognitive, metacognitive, and neuropsychological variables
Bibliographic record
Abstract
The purpose of this study was to examine the cognitive and neuropsychological constructs that are conceptually related to poor insight in obsessive-compulsive disorder (OCD). The relationship between dimensions of insight (Brown Assessment of Beliefs Scale; BABS) and cognitive (magical thinking, paranoia/suspiciousness), metacognitive (metacognition, decentering, cognitive flexibility), and neuroopsychological indices of cognitive flexiblity were examined. Participants with OCD (N = 80) referred for treatment at an outpatient anxiety disorders clinic completed a clinical interview, a brief battery of neuropsychological measures, and a computer-administered questionnaire package assessing the variables of interest. Lower metacognition (i.e., Beck Cognitive Insight Scale [BCIS], composite score) was significantly associated with poorer insight (BABS total; ρ = -.38), and Metacognitions Questionnaire-30 cognitive self-consciousness subscale was negatively correlated with insight regarding a psychiatric source for one’s symptoms (ρ = -.24). Stroop interference was the only neuropsychological variable associated with BABS total score (ρ = -.23), but was not a unique predictor of insight in a regression with BCIS composite scores predicting insight. Nearly all of the variance in insight was accounted for by BCIS composite scores (R = .43, R2 = .18), indicating that metacognition, but not cognitive flexibility, contributes most strongly to clinical insight. Finally, insight decreased when OCD symptoms were activated for both the good and poor insight groups, F(1,78) = 119.29, p < .001, partial η2 = .61, and did not significantly vary as a function of insight group status, F(1, 78) = 3.24, p = .08, partial η2 = .04. Implications, limitations, and directions for future research are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".