The Impact of Emotion Regulation Improvements on Intolerance of Uncertainty During Emotion Regulation Therapy
Bibliographic record
Abstract
Both intolerance of uncertainty (IU) and impoverished emotion regulation repertoires characterize generalized anxiety disorder (GAD). Across two treatment studies, we explored relationships between two emotion regulation skills, decentering and reappraisal, and IU during emotion regulation therapy (ERT). Participants were treatment-seeking individuals diagnosed with GAD. Study 1 included data from two open trials of ERT (N = 52), and Study 2 examined data from a randomized controlled trial of ERT (n = 28) versus a minimal attention control (n = 25). IU and emotion regulation skills were measured at pre-, mid-, and post-treatment. Mediation models explored indirect effects of emotion regulation skills on the relationship between time (Study 1) or group (Study 2) and intolerance of uncertainty. Results demonstrated improvements in emotion regulation skills and reductions in IU during ERT. Greater use of reappraisal and decentering was associated with reduced IU over time. Tests of indirect effects suggested that observed between-group differences in IU can be explained by changes in emotion regulation skills. The findings from these studies highlight the utility of non-IU-specific interventions to help individuals tolerate uncertainty. Exploring the impact of emotion regulation skills on IU could lead to improvements in treating 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".