The Role of Anger in Generalized Anxiety Disorder
Bibliographic record
Abstract
Little is known about the role of anger in the context of anxiety disorders, particularly with generalized anxiety disorder (GAD). The aim of study was to examine the relationship between specific dimensions of anger and GAD. Participants (N=381) completed a series of questionnaires, including the Generalized Anxiety Disorder Questionnaire (GAD-Q-IV; Newman et al., 2002, Behavior Therapy, 33, 215-233), the State-Trait Anger Expression Inventory (STAXI-2; Spielberger 1999, State-Trait Anger Expression Inventory-2: STAXI-2 professional manual, Odessa, FL: Psychological Assessment Resources) and the Aggression Questionnaire (AQ; Buss & Perry 1992, Journal of Personality and Social Psychology, 63, 452-459). The GAD-Q-IV identifies individuals who meet diagnostic criteria for GAD (i.e. GAD analogues) and those who do not (non-GAD). The STAXI-2 includes subscales for trait anger, externalized anger expression, internalized anger expression, externalized anger control and internalized anger control. The AQ includes subscales for physical aggression, verbal aggression, anger and hostility. The GAD-Q-IV significantly correlated with all STAXI-2 and AQ subscales (r's ranging from .10 to .46). Multivariate analyses of variance revealed that GAD analogues significantly differed from non-GAD participants on the combined STAXI-2 subscales (η2=.098); high levels of trait anger and internalized anger expression contributed the most to GAD group membership. GAD analogue participants also significantly differed from non-GAD participants on the combined AQ subscales (η2=.156); high levels of anger (affective component of aggression) and hostility contributed the most to GAD group membership. Within the GAD analogue group, the STAXI-2 and AQ subscales significantly predicted GAD symptom severity (R2=.124 and .198, respectively). Elevated levels of multiple dimensions of anger characterize individuals who meet diagnostic criteria 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 | 0.005 |
| 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 | 0.001 |
| 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".