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Record W4391260042 · doi:10.1080/10615806.2024.2307466

To avoid or not to avoid: impact of self-compassion on safety behaviors in social situations

2024· article· en· W4391260042 on OpenAlexafffund
Kamila A. Szczyglowski, Nancy L. Kocovski

Bibliographic record

VenueAnxiety Stress & Coping · 2024
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyCompassionSocial psychologyApplied psychologyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.041
GPT teacher head0.407
Teacher spread0.366 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes2
Has abstractyes

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