Hierarchical Model of Vulnerabilities for Anxiety: Replication and Extension with a Clinical Sample
Bibliographic record
Abstract
This study served to replicate and extend our previously obtained hierarchical model of the relationships among general anxiety vulnerabilities, specific anxiety vulnerabilities and specific anxiety manifestations including panic symptoms, health anxiety, obsessive-compulsive symptoms and worry. Questionnaires assessing these variables, as well as positive affectivity and depressiveness, were administered to 125 outpatients seeking treatment for panic disorder, social anxiety disorder, obsessive-compulsive disorder, generalized anxiety disorder or major depressive disorder. The results, using a clinical sample, were highly consistent with the hierarchical model obtained in the previous study using a student sample. A more elaborate model, based on published theoretical and empirical evidence, was identified and tested, and similar results were obtained. Negative affectivity had expected direct positive effects on all of the specific anxiety and depression manifestations, with the exception of health anxiety, which showed a negative relationship, and OCD symptoms, which showed no relationship. Positive affectivity was found to be a specific risk factor for depression, while intolerance of uncertainty was found to be a specific risk factor for worry and depression. Finally, anxiety sensitivity appears to be a significant risk factor for panic and health anxiety.
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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 | 0.033 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".