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Record W1994494844 · doi:10.1002/per.452

Separation, self‐disclosure, and social evaluation anxiety as facets of trait social anxiety

2002· article· en· W1994494844 on OpenAlexafffund
Norman S. Endler, Gordon L. Flett, Sophia Macrodimitris, Kimberly Corace, Nancy L. Kocovski

Bibliographic record

VenueEuropean Journal of Personality · 2002
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologySocial anxietyAnxietyWorryTraitDevelopmental psychologyTrait anxietyInterpersonal communicationClinical psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

In the current article, we propose an expansion of the trait anxiety concept to include interpersonal or social facets of trait anxiety involving separation from significant others and disclosing aspects of the self to others, as a supplement to the existing focus on social evaluation anxiety. Participants in three studies completed a modified version of the Endler Multidimensional Anxiety Scales that included a measure of trait social evaluation anxiety, as well as new measures of trait separation anxiety and trait self‐disclosure anxiety (i.e., three measures of trait social anxiety). Results showed that the social evaluation, separation, and self‐disclosure trait anxiety scales have strong psychometric properties and that they represent distinct but related components of trait anxiety. With respect to validity, the facets of trait social anxiety were predictive of related variables including self‐concealment, anxiety sensitivity, and trait worry. The theoretical and practical implications of a multifaceted approach to trait social anxiety are discussed in terms of an expanded multidimensional interaction model of anxiety. Copyright © 2002 John Wiley & Sons, Ltd.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.644
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.081
GPT teacher head0.369
Teacher spread0.288 · 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

Citations22
Published2002
Admission routes2
Has abstractyes

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