Emotion regulation mediates the relation between intolerance of uncertainty and emotion difficulties: A longitudinal investigation
Bibliographic record
Abstract
Intolerance of uncertainty has been proposed as a transdiagnostic factor in emotional disorders. Despite comprehensive empirical evidence demonstrating the association between intolerance of uncertainty and emotional disorders, the underlying mechanism remains elusive. Drawing on theoretical frameworks and empirical studies, the current study proposed that emotion regulation emerges as a potential mechanism. We explored the connections among intolerance of uncertainty, eight emotion regulation strategies (both adaptive and maladaptive), and emotional difficulties (specifically anxiety and depression) using a three-wave longitudinal approach (N = 341). Our findings revealed that heightened intolerance of uncertainty predicted increased anxiety but not depression over time. Greater intolerance of uncertainty significantly predicted elevated levels of maladaptive emotion regulation strategies including experiential avoidance, thought suppression, rumination, and reassurance-seeking. Adaptive strategies (i.e., mindfulness, cognitive reappraisal, problem-solving) predicted lower anxiety and/or depression whereas maladaptive emotion regulation strategy rumination predicted greater levels of anxiety and depression. Surprisingly, thought suppression predicted lower levels of anxiety and depression. More importantly, our analysis showed that both rumination and thought suppression served as significant mediators in the relationship between intolerance of uncertainty and both anxiety and depression. These results hold implications for future interventions, emphasising rumination and thought suppression as potential targets for interventions aimed at alleviating emotional difficulties in individuals with intolerance of uncertainty.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".