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Record W4415478047 · doi:10.1521/pedi.2025.39.5.355

Examining the Convergence of Clinicians’ Ratings of Maladaptive Personality Traits and Categorical Diagnostic Criteria of Borderline Personality Disorder

2025· article· en· W4415478047 on OpenAlexaboutno aff
Ana Carolina de Araujo Oliveira, Craig Rodriguez-Seijas

Bibliographic record

VenueJournal of Personality Disorders · 2025
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsnot available
Fundersnot available
KeywordsBorderline personality disorderPersonalityBig Five personality traitsEmotional dysregulationTraitConvergence (economics)Negative affectivityCategorical variableFacet (psychology)

Abstract

fetched live from OpenAlex

Maladaptive personality trait domains of negative affectivity, antagonism, and disinhibition undergird the borderline personality disorder (BPD) diagnosis according to the Alternative Model for Personality Disorders (AMPD). The current study compared the associations among clinicians’ ratings of maladaptive personality traits, categorical BPD diagnostic criteria, and the global BPD diagnosis in response to a clinical vignette using a large, diverse sample (N = 426) of licensed mental health care practitioners in the United States and Canada. Negative affectivity emerged as the most consistent predictor of BPD criteria and the global diagnosis. At the trait facet level, emotional lability was the most consistent independent predictor of BPD criteria and diagnosis. These findings suggest that clinicians’ agreement with BPD diagnostic criteria corresponds with their perceptions of the patients as experiencing problems related to instability of emotional experiences, feelings of nervousness and frequent worry, and a tendency to engage in dangerous and risky behaviors.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.353
Teacher spread0.315 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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