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Record W1978090858 · doi:10.1037/a0013795

Additional evidence for a quantitative hierarchical model of mood and anxiety disorders for DSM-V: The context of personality structure.

2008· article· en· W1978090858 on OpenAlexaff
Jennifer L. Tackett, Lena C. Quilty, Martin Sellbom, Neil A. Rector, R. Michael Bagby

Bibliographic record

VenueJournal of Abnormal Psychology · 2008
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyPersonality pathologyPersonalityPersonality disordersPsychopathologyClinical psychologyAnxietyContext (archaeology)MoodMood disordersBipolar disorderPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Recent progress toward the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders includes a proposed quantitative hierarchical structure of internalizing pathology with substantial, supportive evidence (D. Watson, 2005). Questions about such a taxonomic shift remain, however, particularly regarding how best to account for and use existing diagnostic categories and models of personality structure. In this study, the authors use a large sample of psychiatric patients with internalizing diagnoses (N = 1,319) as well as a community sample (N = 856) to answer some of these questions. Specifically, the authors investigate how the diagnoses of obsessive-compulsive disorder (OCD) and bipolar disorder compare with the other internalizing categories at successive levels of the personality hierarchy. Results suggest unique profiles for bipolar disorder and OCD and highlight the important contribution of a 5-factor model of personality in conceptualizing internalizing pathology. Implications for personality-psychopathology models and research on personality structure are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.097
GPT teacher head0.390
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations115
Published2008
Admission routes1
Has abstractyes

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