An in-depth examination of intolerance of uncertainty and its modification in generalized anxiety disorder
Bibliographic record
Abstract
Two decades of research support Dugas and colleagues’ (1998) Intolerance of Uncertainty (IU) Model of Generalized Anxiety Disorder (GAD), suggesting that IU is a key factor involved in the maintenance of excessive worry. As such, a cognitive behavioural treatment targeting IU (CBT-IU) has been developed, and it is a highly efficacious treatment for GAD. Given the importance of IU in GAD, the current research investigated attitudes and behaviours associated with uncertainty that are not yet well understood, and tested the effects of a technique employed in CBT-IU called uncertainty exposure on IU and GAD symptoms. Study 1a and 1b present the results of a mixed methods study that involved asking individuals with GAD (n = 20) and non-psychiatric control participants (NPCs; n = 20) about their experience with uncertainty, coding their responses into themes, and examining between-group differences in responses. Study 1a compared the responses of people with GAD to NPCs on: beliefs about uncertainty, attitudes toward different “types” of uncertainty, and reflections on clinical observations about IU. Study 1b compared the responses of the GAD and NPC groups on: what behaviours they engage in when uncertain, as well as the frequency, functions, discontinuation factors, and problems associated with such behaviours. Study 1a and 1b produced rich data that offer novel insights about the nature of IU in GAD. Study 2 tested the impact of completing a single session of training in uncertainty exposure and 1 week of practice with uncertainty exposure (exposure group; n = 20), compared to completing assessment only (control iv group; n = 20) in a GAD sample. Participants in the exposure group showed large significant improvements in IU and GAD symptoms from baseline to 1 or 2 weeks post baseline, whereas the control group showed no change. Completing more uncertainty exposure practice was associated with larger improvement in outcomes. These findings provide novel support for the proposed role of IU in maintaining GAD, and for the use of uncertainty exposure in GAD treatment. Overall, findings from this dissertation inform theoretical conceptualizations of IU in GAD and the clinical application of this model.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".