Intolerance of Uncertainty and Emotion Dysregulation as Predictors of Generalized Anxiety Disorder Severity in a Clinical Population
Bibliographic record
Abstract
Background/objectives: Several factors have been shown to play a role in the development and maintenance of generalized anxiety disorder (GAD), including intolerance of uncertainty and emotion dysregulation. Although the individual contribution of both of these factors is well documented, their combined effect has yet to be studied in a clinical population with GAD. The aim of the present study was to examine the relative contribution of intolerance of uncertainty and emotion dysregulation to the prediction of worry and GAD severity in adults with GAD. Methods: The sample consisted of 108 participants diagnosed with GAD. The participants completed measures of worry, GAD severity, depressive symptoms, intolerance of uncertainty, and emotion dysregulation. Results: Multiple regression indicated that both intolerance of uncertainty and emotion dysregulation significantly contributed to both worry and GAD severity, over and above the contribution of depressive symptoms. Of note, the model explained 36% of the variance in GAD severity scores. Conclusions: The present results provide preliminary evidence of complementarity among dominant models of GAD, and point to the potential role of integrative conceptualizations and treatment strategies for 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 | 0.001 | 0.004 |
| 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) | 0.001 | 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".