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Record W2918679067 · doi:10.1037/per0000327

Clarifying the interstitial nature of facets from the Personality Inventory for DSM–5 using the five factor model of personality.

2019· article· en· W2918679067 on OpenAlexfundno aff
Carolyn A Watters, Martin Sellbom, Amanda A. Uliaszek, R. Michael Bagby

Bibliographic record

VenuePersonality Disorders Theory Research and Treatment · 2019
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of TorontoAmerican Psychiatric Association
KeywordsPsychologyOpenness to experienceFacet (psychology)Negative affectivityConscientiousnessPositive affectivityBig Five personality traitsPersonalityStructural equation modelingPersonality Assessment InventoryClinical psychologyPersonality disordersDevelopmental psychologyExtraversion and introversionSocial psychologyStatistics

Abstract

fetched live from OpenAlex

= 388). Our findings revealed an optimal five-factor structure in the undergraduate sample (in which Openness facets did not load on any factor) and a six-factor structure in the clinical sample (in which Openness formed its own factor). Domains displayed good convergent validity with the domains of the personality psychopathology five model, except for Disinhibition/Conscientiousness, in which the lack of convergence was explained by Conscientiousness. Furthermore, we evaluated six specific PID-5 facets with respect to interstitiality and optimal PID-5 domain placement, where results supported several recommendations for model modification of the PID-5 structure. These include moving Restricted Affectivity to Detachment from Negative Affectivity, moving Hostility to Antagonism from Negative Affectivity, moving Suspiciousness to Negative Affectivity from Detachment, and removing Submissiveness from the PID-5 measure and the alternative model of personality disorders. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.003
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 head0.135
GPT teacher head0.418
Teacher spread0.283 · 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 designQualitative
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

Citations20
Published2019
Admission routes1
Has abstractyes

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