The relationship between negative urgency and generalized anxiety disorder symptoms: the role of intolerance of negative emotions and intolerance of uncertainty
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: GAD symptoms are associated with greater negative urgency, a dimension of impulsivity defined as the tendency to act rashly when distressed. This study examined the degree to which intolerance of negative emotional states and intolerance of uncertainty account for the association between negative urgency and GAD symptoms. DESIGN: An analysis of indirect effects evaluated whether intolerance of negative emotions and intolerance of uncertainty uniquely account for the association between negative urgency and GAD symptom severity. METHODS: Undergraduate students (N = 308) completed measures of GAD symptoms, trait anxiety, negative urgency, distress tolerance, and intolerance of uncertainty. RESULTS: Greater symptoms of GAD, intolerance of negative emotional states, and intolerance of uncertainty were associated with greater negative urgency. There was an indirect relationship between negative urgency and GAD symptoms through intolerance of negative emotional states and intolerance of uncertainty even when controlling for trait anxiety. Intolerance of negative emotional states and intolerance of uncertainty each had an indirect relationship with GAD severity through negative urgency, suggesting possible bi-directional relations. CONCLUSIONS: Future studies should examine the role of intolerance of negative emotional states and intolerance of uncertainty in the impulsive behavior of individuals with GAD, and whether impulsive behavior reinforces these processes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".