Generalized Anxiety Disorder: Does the Emotion Dysregulation Model Predict Symptoms Above the Metacognitive Model?
Bibliographic record
Abstract
Individuals with generalized anxiety disorder (GAD) experience excessive anxiety and uncontrollable worry lasting at least six months, with their worry not limited to a single type or context. The Metacognitive Model (MCM) is a well-established framework of GAD, emphasizing negative beliefs about worry (NB). Recently, the Emotion Dysregulation Model (EDM) of GAD was proposed, focusing on issues with understanding, expressing, and managing emotions. However, there is a lack of research examining the utility of the EDM by comparing it to more established models, such as the MCM. This study extends the current literature by examining whether the EDM helps explain GAD symptoms when compared to the MCM. Self-report measures of worry, GAD symptoms, the EDM, and the MCM were administered to a non-clinical university sample (N = 400). The following was hypothesized: GAD symptoms and worry would positively correlate with emotion dysregulation; GAD symptoms and worry would positively correlate with NB; and emotion dysregulation would predict GAD symptoms independently of NB. Bivariate correlations were conducted to determine whether measures of the MCM and EDM correlated with GAD and worry. Regression analyses tested which measures uniquely predicted GAD and worry. Findings demonstrated that NB and various components of the EDM correlate with GAD and worry. Regression analyses found that when controlling for NB, emotional expressivity (EE) and fear of emotions predicted GAD, while EE and difficulty regulating emotions predicted worry. Findings have implications for GAD treatment, encouraging an approach including emotion psychoeducation and development of effective emotional regulation strategies. Discipline: Psychology (Honours) Faculty Mentor: Dr. Alexander Penney
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".