To avoid or not to avoid: impact of self-compassion on safety behaviors in social situations
Bibliographic record
Abstract
Background/Objectives: Safety behaviors are commonly used to decrease anxiety in social settings but maintain anxiety. Self-compassion has been shown to reduce anxiety and rumination, but the impact on safety behaviors has not been examined. For the present studies, it was hypothesized that inducing self-compassion would lead to lower safety behaviors compared to controls.Methods: In Study 1 (N = 390), participants with elevated social anxiety recalled a distressing social situation, were randomly assigned to a self-compassionate (n = 186) or control (n = 204) writing exercise, and then reported predicted self-compassion and safety behaviors for a future situation. In Study 2 (N = 114), the impact of self-compassionate (n = 56) or control writing (n = 58) on safety behaviors was investigated during a Zoom interaction.Results/Conclusions: In Study 1, as hypothesized, the self-compassion condition reported fewer expected avoidance behaviors compared to controls. In Study 2, state self-compassion and safety behaviors did not differ between conditions. In both studies, distress significantly mediated the relationship between condition and safety behaviors, such that the self-compassion condition reported significantly lower distress, which was associated with lower safety behaviors. Future research can examine whether reduced distress and safety behaviors allow for greater social connection.
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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 | 0.002 |
| 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 | 0.001 |
| 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".