MétaCan
Menu
Back to cohort
Record W1989881139 · doi:10.1080/16506073.2014.961539

Cognitive Constructs and Social Anxiety Disorder: Beyond Fearing Negative Evaluation

2014· article· en· W1989881139 on OpenAlexaff
Michelle J. N. Teale Sapach, R. Nicholas Carleton, Myriah K. Mulvogue, Justin W. Weeks, Richard G. Heimberg

Bibliographic record

VenueCognitive Behaviour Therapy · 2014
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsFear of negative evaluationAnxiety sensitivityPsychologySocial anxietyAnxietyCognitionVariance (accounting)Clinical psychologyStructural equation modelingPsychiatry

Abstract

fetched live from OpenAlex

Pioneering models of social anxiety disorder (SAD) underscored fear of negative evaluation (FNE) as central in the disorder's development. Additional cognitive predictors have since been identified, including fear of positive evaluation (FPE), anxiety sensitivity, and intolerance of uncertainty (IU), but rarely have these constructs been examined together. The present study concurrently examined the variance accounted for in SAD symptoms by these constructs. Participants meeting criteria for SAD (n = 197; 65% women) completed self-report measures online. FNE, FPE, anxiety sensitivity, and IU all accounted for unique variance in SAD symptoms. FPE accounted for variance comparable to FNE, and the cognitive dimension of anxiety sensitivity and the prospective dimension of IU accounted for comparable variance, though slightly less than that accounted for by FNE and FPE. The results support the theorized roles that these constructs play in the etiology of SAD and highlight both FNE and FPE as central foci in SAD treatment.

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)0.006
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.367
Teacher spread0.317 · 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

Citations63
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCognitive Behaviour TherapySame topicAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesFrench-language works237,207