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Record W2131988763 · doi:10.1002/da.20081

Psychometric validation of a monitoring-blunting measure for social anxiety disorder: the coping styles questionnaire for social situations (CSQSS)

2005· article· en· W2131988763 on OpenAlexaff
Peter G. Mezo, Randi E. McCabe, Martin M. Antony, Kim Burns

Bibliographic record

VenueDepression and Anxiety · 2005
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsPsychologySocial anxietyAnxietyExploratory factor analysisConstruct validityCoping (psychology)Clinical psychologyPsychometricsSocial supportTest validityPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

The purpose of this investigation was to conduct a psychometric validation of the Coping Styles Questionnaire for Social Situations (CSQSS). The CSQSS was developed to measure monitoring and blunting coping styles in social situations based on Miller's conceptualization of how individuals cope with threat-related information. Study 1 evaluated the content validity of the CSQSS monitoring and blunting items. Study 2 examined factor structure, reliability, and construct validity of the CSQSS in a sample of 443 college students. Evidence supported the content and face validity of the CSQSS. In addition, an exploratory factor analysis revealed a two-factor solution consistent with the monitoring and blunting constructs. Both monitoring and blunting scores were positively correlated with measures of social anxiety, with blunting having a stronger relationship. Moreover, individuals with high social anxiety engaged in a significantly higher degree of monitoring and blunting than did individuals with low social anxiety. Taken together, these results provide support for the reliability and validity of the CSQSS. The CSQSS may serve as a useful measure for further examination of monitoring and blunting coping styles in a social anxiety disorder sample.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.365
Teacher spread0.315 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

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