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Record W2972308946 · doi:10.1097/nmd.0000000000001044

Interpersonal Problems Associated With Passive-Aggressive Personality Disorder

2019· article· en· W2972308946 on OpenAlexaff
Olivier Laverdière, John S. Ogrodniczuk, David Kealy

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2019
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of British ColumbiaUniversité de Sherbrooke
Fundersnot available
KeywordsPsychologyConstruct (python library)PersonalityInterpersonal communicationPersonality pathologyPsychopathologyClinical psychologyDistressPersonality disordersBorderline personality disorderPsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

With a controversial history, passive-aggressive personality disorder (PAPD) was eventually removed from the latest edition of the Diagnostic and Statistical Manual for Mental Disorders. Despite its demise from diagnostic nomenclature, clinicians continue to regard it as a clinically relevant construct, and some researchers argue for its resurrection. Toward this end, it is important to empirically demonstrate the relevance of the passive-aggressive personality construct, including demonstrating its association with impaired functioning. Consistent with contemporary emphasis on interpersonal functioning in personality pathology, the current study aims to explore interpersonal problems that are associated with PAPD in a large clinical sample. Before beginning treatment, 240 patients completed assessments of personality psychopathology and interpersonal functioning. Results showed that higher levels of PAPD were significantly associated with greater level of interpersonal distress, especially regarding interpersonal problems of a vindictive nature. The findings are consistent with clinical descriptions of the core conflictual relational issues of patients with PAPD and lend some support to further considering PAPD as a valid diagnostic construct.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.011
GPT teacher head0.269
Teacher spread0.258 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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