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Record W2767562270 · doi:10.1177/0033294117740138

Examining the Relationships Among Self-Compassion, Social Anxiety, and Post-Event Processing

2017· article· en· W2767562270 on OpenAlexaff
Rebecca A. Blackie, Nancy L. Kocovski

Bibliographic record

VenuePsychological Reports · 2017
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPsychologyComplex event processingSocial anxietySelf-compassionCompassionShynessAnxietyDevelopmental psychologyMindfulnessSocial psychologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Post-event processing refers to negative and repetitive thinking following anxiety provoking social situations. Those who engage in post-event processing may lack self-compassion in relation to social situations. As such, the primary aim of this research was to evaluate whether those high in self-compassion are less likely to engage in post-event processing and the specific self-compassion domains that may be most protective. In study 1 ( N = 156 undergraduate students) and study 2 ( N = 150 individuals seeking help for social anxiety and shyness), participants completed a battery of questionnaires, recalled a social situation, and then rated state post-event processing. Self-compassion negatively correlated with post-event processing, with some differences depending on situation type. Even after controlling for self-esteem, self-compassion remained significantly correlated with state post-event processing. Given these findings, self-compassion may serve as a buffer against post-event processing. Future studies should experimentally examine whether increasing self-compassion leads to reduced post-event processing.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.116
GPT teacher head0.386
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations55
Published2017
Admission routes1
Has abstractyes

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