Supplemental Material for Robust Dimensions of Anxiety Sensitivity: Development and Initial Validation of the Anxiety Sensitivity Index—3
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.015 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".