Predicting Generalized Anxiety Disorder Based on Emotion Regulation Deficits, Thought-Action Fusion, and Behavioral Inhibition
Bibliographic record
Abstract
Background & Aims: Generalized anxiety disorder (GAD) can be affected by different emotional, cognitive, and natural factors. The purpose of this study was to predict GAD based on emotion regulation deficits, thought-action fusion, and behavioral inhibition. \nMethods: This was a correlational study. The study sample was comprised of 135 patients with GAD selected from among patients who were referred to psychiatric clinics and centers in Tabriz, Iran, using convenient sampling method. The data collection tool consisted of cognitive emotion regulation questionnaire (CERQ), thought fusion instrument (TFI), Beck Anxiety Inventory (BDI), retrospective and adults measures of behavioral inhibition (RMBI, AMBI), and the 20-item Toronto Alexithymia Scale (TAS-20). Data were analyzed using Pearson correlation coefficient and stepwise multiple regression analysis method. \nResults: Difficulty in describing and identifying emotions, maladaptive emotion regulation strategies, thought-action fusion, and childhood behavioral inhibition had significant relationships with GAD \n(P < 0.010). Moreover, stepwise regression analysis showed that difficulty in identifying emotions, maladaptive emotion regulation, thought-action fusion, and childhood behavioral inhibition were the best predictors for GAD, respectively. \nConclusion: Considering the role of emotion regulation deficits, thought-action fusion, and childhood behavioral inhibition in GAD, these variables must be taken into account in the prevention and treatment programs for GAD. \n \n \nKeywords: Emotion regulation deficits, Thought-action fusion, Behavioral inhibition, Generalized anxiety disorder
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".