The interaction of distress tolerance and intolerance of uncertainty in the prediction of symptom reduction across CBT for social anxiety disorder
Bibliographic record
Abstract
Distress tolerance (DT) and intolerance of uncertainty (IU) have been identified as transdiagnostic processes that predict symptom severity across a range of distinct anxiety disorders. However, the joint effect of these two variables on therapeutic outcome has not yet been examined. It is possible that DT and IU may both impact on treatment response to cognitive-behavioural therapy (CBT) in clients with anxiety, as clients with weak DT and strong IU may be less likely to engage in exposure and cognitive restructuring tasks across treatment due to their associated distress. The purpose of this study was to examine the interaction of DT and IU as predictors of post-treatment symptom severity and treatment response to group CBT in participants with primary DSM-IV-TR diagnosed social anxiety disorder (SAD). Participants (N = 95) with SAD completed 12 weeks of manualized group CBT. Results of multilevel longitudinal analysis demonstrated an interaction effect, such that lower DT and higher IU predicted higher SAD symptom severity across the course of therapy. The findings are discussed in terms of clinical implications for the disorder-specific and transdiagnostic treatment of anxiety disorders.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".