Letting go of yesterday: Effect of distraction on post-event processing and anticipatory anxiety in a socially anxious sample
Bibliographic record
Abstract
According to cognitive models, post-event processing (PEP) is a key factor in the maintenance of social anxiety. Given that decreasing PEP can be challenging for socially anxious individuals, it is important to identify potentially useful strategies. Although distraction may help to decrease PEP, the findings have been equivocal. The primary purpose of this study was to examine whether a brief distraction period immediately following a speech would lead to less PEP the next day. The secondary aim was to examine the effect of distraction following an initial speech on anticipatory anxiety for a second speech, via reductions in PEP. Participants (N = 77 undergraduates with elevated social anxiety; 67.53% female) delivered a speech and were randomly assigned to a distraction, rumination, or control condition. The following day, participants reported levels of PEP in relation to the first speech, as well as anxiety regarding a second, upcoming speech. As expected, those in the distraction condition reported less PEP than those in the rumination and control conditions. Additionally, distraction following the first speech was indirectly related to anticipatory anxiety for the second speech, via PEP. Distraction may represent a potentially useful strategy for reducing PEP and other maladaptive processes that may maintain social anxiety.
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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 | 0.005 |
| 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".