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Record W2192919283 · doi:10.1080/14999013.2015.1114535

Are Psychopathic and Borderline Personality Disorder Distinct, or Differently Gendered Expressions of the Same Disorder? An Exploration Using Concept Maps

2015· article· en· W2192919283 on OpenAlexafffund
Simone Viljoen, Alana N. Cook, Yan L. Lim, Brianne K. Layden, N. Kate Bousfield, Stephen D. Hart

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2015
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsBC Mental Health & Substance Use ServicesSt. Joseph’s Healthcare HamiltonSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBorderline personality disorderPsychologyConceptualizationPsychopathyPersonality disordersPersonalityConstruct (python library)Antisocial personality disorderMental healthClinical psychologyPsychoanalysisPsychiatryPoison controlInjury preventionMedicine

Abstract

fetched live from OpenAlex

Research findings on gender differences in prevalence and clinician gender bias in the diagnosis of Psychopathic Personality Disorder (PPD) and Borderline Personality Disorder (BPD) have led some to suggest that PPD and BPD are not distinct disorders, but rather differently gendered expressions of the same disorder. This paper explores gender differences in conceptualization using prototypicality ratings of PPD and BPD symptoms from the Comprehensive Assessment of Psychopathic Personality (CAPP; Cooke, Hart, Logan, & Michie, 2004 , 2012 ) and the Comprehensive Assessment of Borderline Personality (CABP; Cook et al., 2013 ). Findings indicated that symptoms of PPD and BPD are gendered, but did not appear consistent with the view that PPD and BPD are differently gendered variants of the same disorder. Unfortunately, the implications of our findings for clinical practice are not clear at this time. Future research should explore effective methods to mitigate clinician bias and/or explore the development of gender-fair measures.

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 score0.162
Threshold uncertainty score0.599

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 head0.140
GPT teacher head0.418
Teacher spread0.278 · 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

Citations19
Published2015
Admission routes2
Has abstractyes

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