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Record W4387472498 · doi:10.1177/00332941231204306

Associating Internalizing and Externalizing Symptom Features With the Personality Inventory for DSM-5 Brief Form (PID-5-BF) in a Large Community Sample

2023· article· en· W4387472498 on OpenAlexaffabout
Sylvia M. L. Cox, Robyn J. McQuaid, Ayotola Ogunlana, Natalia Jaworska

Bibliographic record

VenuePsychological Reports · 2023
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of OttawaMcGill UniversityCarleton UniversityDawson College
Fundersnot available
KeywordsPsychologyPsychoticismSensation seekingBig Five personality traitsClinical psychologyAnxietyImpulsivityPersonalityMoodNegative affectivityCannabisPsychiatryExtraversion and introversionSocial psychology

Abstract

fetched live from OpenAlex

= 661 Canadian adults in the community. Depression, anxiety, and perceived stress measures were obtained, as were indices of alcohol and cannabis use. Symptoms of depression and perceived stress were associated with all PID-5-BF dimensions, except for antagonism. Anxiety symptoms were associated with negative affectivity (NA) and, to a lesser extent, psychoticism. A younger age and female sex were related to higher depression and stress scores. In contrast to the models assessing depressive, anxiety and stress symptoms, in which NA was the strongest contributor, no significant contribution of internalizing traits (i.e., PID-5-BF NA) on substance use outcomes was found when externalizing traits were included in the models. Specifically, binge drinking and cannabis use were associated with higher disinhibition scores and lower psychoticism scores. Regression models were substantially weaker for substance use than for the mood and stress symptoms. Younger individuals used more cannabis and males engaged in more binge drinking. These findings largely confirm PID-5-BF's construct validity, and indicate that various indices of wellbeing (not necessarily personality-associated measures) are associated with personality traits, as measured with the brief from of PID-5-BF.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.014
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.383
Teacher spread0.318 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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