Intolerance of Uncertainty Mediates the Relation Between Generalized Anxiety Disorder Symptoms and Anger
Bibliographic record
Abstract
Previous research has shown that individuals with generalized anxiety disorder (GAD) report elevated anger compared with nonanxious individuals; however, the pathways linking GAD and anger are currently unknown. We hypothesized that negative beliefs about uncertainty, negative beliefs about worry and perfectionism dimensions mediate the relationship between GAD symptoms and anger variables. We employed multiple mediation with bootstrapping on cross-sectional data from a student sample (N = 233) to test four models assessing potential mediators of the association of GAD symptoms to inward anger expression, outward anger expression, trait anger and hostility, respectively. The belief that uncertainty has negative personal and behavioural implications uniquely mediated the association of GAD symptoms to inward anger expression (confidence interval [CI] = .0034, .1845, PM = .5444), and the belief that uncertainty is unfair and spoils everything uniquely mediated the association of GAD symptoms to outward anger expression (CI = .0052, .1936, PM = .4861) and hostility (CI = .0269, .2427, PM = .3487). Neither negative beliefs about worry nor perfectionism dimensions uniquely mediated the relation of GAD symptoms to anger constructs. We conclude that intolerance of uncertainty may help to explain the positive connection between GAD symptoms and anger, and these findings give impetus to future longitudinal investigations of the role of anger in GAD.
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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 | 0.014 |
| 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 | 0.001 |
| 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".