Intolerance of Uncertainty as a Contributor to Fear and Avoidance Symptoms of Panic Attacks
Bibliographic record
Abstract
Panic disorder symptoms are persistent for 50-80% of cases even after treatment, resulting in experiences of disability and dissatisfaction in life. Previous research has focused on anxiety sensitivity (AS) and its dimensions as contributing to symptoms of panic disorder; however, recent research has suggested that intolerance of uncertainty (IU)-the tendency for a person to consider the possibility of a negative event occurring as threatening, irrespective of the actual probability of its occurrence-may also play a critical role. The current study was designed to assess the specific relationships between dimensions of IU (i.e. prospective IU and inhibitory IU) and the fear and avoidance symptoms associated with panic disorder. Participants included 122 community members (81% women) with a history of at least one panic attack who participated in a larger study on fear. Participants completed measures of AS, IU, and panic disorder symptoms. Correlation and regression analyses supported a significant and substantial relationship between AS, inhibitory IU, and panic disorder symptoms. Inhibitory IU accounted for relatively more variance in avoidance symptoms related to panic disorder than did the fears of physical sensations dimension of AS. As such, further investigation of the role of IU in panic disorder symptoms appears warranted. Comprehensive results, implications, and directions for future research are discussed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".