On the hierarchical structure of mood and anxiety disorders: Confirmatory evidence and elaboration of a model of temperament markers.
Bibliographic record
Abstract
The authors examined D. Watson's (2005) proposed reconceptualization of the diagnostic categories for mood and anxiety disorders for the Diagnostic and Statistical Manual of Mental Disorders--Fifth Edition (DSM-V) and tested an elaboration of the 2-factor (positive and negative activation) model of underlying temperament markers that incorporates A. Tellegen, D. Watson, & L. A. Clark's (1999a, 1999b) higher-order dimension of happiness-unhappiness (or demoralization; see A. Tellegen et al., 2003). In Study 1, 502 undergraduate students completed several symptom measures of mood and anxiety disorders and the Minnesota Multiphasic Personality Inventory-2 (J. N. Butcher et al., 2001). Using confirmatory factor analysis, the authors replicated Watson's distress and fear disorder model. Path analyses showed that demoralization was a primary marker of distress disorders, whereas dysfunctional negative emotions was a primary marker of fear disorders. Low positive emotions was a specific marker of depression and social phobia. This 3-factor path model was associated with better fit than was a 2-factor model excluding demoralization. In Study 2, the authors replicated the findings of Study 1 using data from an archival clinical sample of 636 Veterans Affairs hospital outpatients. The authors' findings provide evidence on the important role of demoralization in mood and anxiety disorders.
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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 | 0.036 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".