An examination of worry and self-distancing as coping strategies for anxiety-provoking experiences in individuals high in worry
Bibliographic record
Abstract
Objectives This preliminary online study investigated the short-term effects of self-distancing, worry, and distraction on anxiety and worry-related appraisals among individuals high in worry.Design and Methods N = 104 community members high in trait worry were randomly assigned to think about a personally identified worry-provoking situation using self-distancing (SC), worry (WC), or distraction (DC). Participants rated their anxiety (Visual Analogue Scale for Anxiety) and appraisals of the situation (Perceived Probability, Coping, and Cost Questions) at post-task and one-day follow-up.Results Mixed factorial ANOVAs revealed an increase in anxiety within the WC (d = .475) and no difference in anxiety within the SC (d = .010) from pre- to post-task. There was no difference in anxiety within the DC (p = .177). Participants within the SC reported a decrease in the perceived cost associated with their identified situation from pre- to post-task (d = .424), which was maintained at one-day follow-up (d = .034). Participants reported an increase in perceived ability to cope from post-task to one-day follow-up (d = .236), and from pre-task to one-day follow-up (d = .338), regardless of condition.Conclusions Self-distancing may prevent increases in anxiety and catastrophizing while reflecting on a feared situation.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".