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Record W2898420803 · doi:10.1002/jclp.22708

Unique and shared features of narcissistic and antisocial personality disorders: Implications for assessing and modeling externalizing traits

2018· article· en· W2898420803 on OpenAlexaff
Kasey Stanton, Mark Zimmerman

Bibliographic record

VenueJournal of Clinical Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyAntisocial personality disorderPsychopathologyBig Five personality traitsPersonality disordersClinical psychologyNarcissistic personality disorderExploratory factor analysisDevelopmental psychologyPersonalityPsychometricsPoison controlSocial psychologyInjury prevention

Abstract

fetched live from OpenAlex

OBJECTIVES: We aimed to determine which, if any, features distinguish antisocial and narcissistic personality disorders (ASPD and NPD), two overlapping externalizing disorders. METHODS: A large sample of outpatients (N = 2,149) completed interview measures assessing personality pathology, other psychopathology, and impairment. The structure of antisocial and narcissistic traits was examined using both exploratory bifactor and traditional exploratory factor analytic approaches, and we examined relations for our emergent factors. RESULTS: Factor analytic results indicated that most narcissistic and antisocial traits were strongly overlapping, although some features emerged as relatively distinct (e.g., arrogance defining NPD). Factors modeling our specific bifactor dimensions showed very weak psychopathology and impairment relations. CONCLUSIONS: The structure of ASPD and NPD traits does not align neatly with Diagnostic and Statistical Manual of Mental Disorders (DSM-5) Section II conceptualizations, Regardless of the factor analytic approach used. Our findings also indicate that specific dimensions defining these PDs show modest predictive power after accounting for a general externalizing dimension.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.235
GPT teacher head0.548
Teacher spread0.313 · 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.

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

Citations10
Published2018
Admission routes1
Has abstractyes

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