Negative Affect Mediates Impulsivity in Generalized Anxiety Disorder
Bibliographic record
Abstract
INTRODUCTION: Although anxiety and impulsivity are intuitively thought to be inversely correlated, increased impulsivity has been associated both with generalized anxiety disorder (GAD) diagnosis and GAD symptoms in non-clinical samples. The emotional dysregulation model of GAD posits that patients experience more frequent and intense negative emotions while having poor regulatory control over emotional states and greater negative reactivity to their emotions. We hypothesized that poor regulatory control in the presence of negative emotions might explain the increased impulsivity found in GAD patients. In this study, we examined if negative affect mediates the relationship between GAD and impulsivity. METHODS: Thirty-four GAD patients and 35 healthy controls were included, and evaluated with measurements of impulsivity, negative and positive emotions, the severity of worrying and GAD symptoms, depression, and 5-factor personality traits. RESULTS: Global impulsivity scores and the attentional facet of impulsivity were higher in the patient group when compared to the controls. Negative affect was correlated with global impulsivity in the patient group only and explained impulsivity in our regression model while worrying and depressive symptoms did not. An indirect relationship was found between diagnosis and impulsivity through negative affect. CONCLUSION: Our study showed that the cardinal symptom of GAD - worrying - was not independently related to impulsivity in our sample. Increased impulsivity in GAD seems to be mediated by the increased presence of negative emotions, as it is common in mood and impulse-control disorders, indicating an unspecific shared vulnerability factor to psychopathology.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".