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Supplemental Material for Robust Dimensions of Anxiety Sensitivity: Development and Initial Validation of the Anxiety Sensitivity Index—3

2007· article· en· W4246511004 on OpenAlexaboutno aff

Bibliographic record

VenuePsychological Assessment · 2007
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsnot available
Fundersnot available
KeywordsAnxiety sensitivityPsychologyAnxietySensitivity (control systems)Index (typography)PsychometricsTest validityClinical psychologyDevelopmental psychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Accumulating evidence suggests that anxiety sensitivity (fear of arousal-related sensations) plays an important role in many clinical conditions, particularly anxiety disorders. Research has increasingly focused on how the basic dimensions of anxiety sensitivity are related to various forms of psychopathology. Such work has been hampered because the original measure-the Anxiety Sensitivity Index (ASI)- was not designed to be multidimensional. Subsequently developed multidimensional measures have unstable factor structures or measure only a subset of the most widely replicated factors. Tlierefore, the authors developed, via factor analysis of responses from U.S. and Canadian nonclinical participants (n = 2,361), an 18-item measure, the ASI-3, which assesses the 3 factors best replicated in previous research: Physical, Cognitive, and Social Concerns. Factorial validity of the ASI-3 was supported by confirmatory factor analyses of 6 replication samples, including nonclinical samples from the United States and Canada, France, Mexico, the Netherlands, and Spain (n = 4,494) and a clinical sample from the United States and Canada (n = 390). The ASI-3 displayed generally good performance on other indices of reliability and validity, along with evidence of improved psychometric properties over the original ASI.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.059
GPT teacher head0.386
Teacher spread0.327 · 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
Published2007
Admission routes1
Has abstractyes

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