Post-Event Processing across Multiple Anxiety Presentations: Is it Specific to Social Anxiety Disorder?
Bibliographic record
Abstract
BACKGROUND: Post-event processing (PEP) occurs when individuals engage in cognitive rumination following an event or interaction. Although the relation between PEP and social anxiety has been clearly demonstrated, it remains unclear whether PEP is limited to individuals with elevated social anxiety, or if it is also problematic among people with other anxiety presentations. AIMS: The present study assessed PEP after the first session of group cognitive behavioural therapy (CBT) in individuals with a variety of anxiety presentations. METHOD: Participants with a principal diagnosis of SAD (N = 25), those diagnosed with a principal other anxiety disorder with comorbid SAD (N = 18), and those with principal other anxiety diagnoses with no SAD (N = 43) completed baseline measures of social anxiety severity and state anxiety at their first session of CBT and measures of PEP one week later. RESULTS: Participants with a principal diagnosis of SAD experienced the most PEP in the week following the first CBT session, while those with no comorbid SAD experienced the least. Those with comorbid SAD experienced intermediate levels of PEP. The strongest predictor of PEP was state anxiety during the first session. CONCLUSIONS: Results suggest that PEP is more problematic for clients with SAD as part of their clinical presentation. Clinical and theoretical implications 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 | 0.002 | 0.010 |
| 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) | 0.002 | 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".