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Record W3175197391 · doi:10.1002/pmh.1522

Analysis of the interaction between personality dysfunction and traits in the statistical prediction of physical aggression: Results from outpatient and community samples

2021· article· en· W3175197391 on OpenAlexafffund
Philippe Leclerc, Claudia Savard, David D. Vachon, Jonathan Faucher, Maude Payant, Mireille Lampron, Marc Tremblay, Dominick Gamache

Bibliographic record

VenuePersonality and Mental Health · 2021
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleCentres Intégré Universitaires de Santé et de Services SociauxUniversité du Québec à MontréalMcGill UniversityUniversité LavalUniversité du Québec à Trois-Rivières
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyPersonalityBig Five personality traitsAggressionTraitPersonality disordersClinical psychologyCategorical variablePersonality Assessment InventoryPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

The Alternative Model for Personality Disorders (AMPD), included in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.) and the World Health Organization's International Classification of Diseases (11th ed.; ICD-11) are, respectively, hybrid categorical-dimensional and dimensional frameworks for personality disorders (PDs). Both models emphasize personality dysfunction and personality traits. Previous studies investigating the links between the AMPD and ICD-11, and self-reported physical aggression have mostly focused on traits and did not take into account the potential interaction between personality dysfunction and traits. Thus, the aim of this study is to identify dysfunction*trait interactions using regression-based analysis. Outpatients with personality disorder from a specialized public clinic (N = 285) and community participants (N = 995) were recruited to complete self-report questionnaires. Some small-size, albeit significant and clinically/conceptually meaningful personality dysfunction*trait interactions were found to predict physical aggression in both samples. Interaction analyses might further inform, to some degree, about the current discussion pertaining to the potential redundancy between dysfunction and traits, the optimal personality dysfunction structure (in the case of the AMPD), as well as clinical assessment based on AMPD/ICD-11 PD frameworks.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.214
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.083
GPT teacher head0.381
Teacher spread0.298 · 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

Citations11
Published2021
Admission routes2
Has abstractyes

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