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Record W2897887299 · doi:10.1037/pas0000655

Cross-validation of the demoralization construct in the Revised NEO Personality Inventory.

2018· article· en· W2897887299 on OpenAlexaffabout
Amanda A. Uliaszek, Nadia Al‐Dajani, Martin Sellbom, R. Michael Bagby

Bibliographic record

VenuePsychological Assessment · 2018
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of TorontoThe Scarborough Hospital
Fundersnot available
KeywordsPsychologyPsycINFOConstruct validityPersonalityConstruct (python library)PsychopathologyPersonality Assessment InventoryPersonality disordersPsychometricsClinical psychologyMental healthStructural equation modelingPsychiatrySocial psychologyMEDLINE

Abstract

fetched live from OpenAlex

Demoralization is defined as a pervasive, generalized negative emotional construct present in psychiatric disorders and a variety of medical conditions. Demoralization is also conceptualized as a ubiquitous affective-laden factor common to most forms of psychopathology that increases the magnitude of intercorrelations among putatively distinct psychiatric symptom scales (Tellegen, 1985). Using exploratory structural equation modeling to identify common variance across the revised NEO Personality Inventory (NEO PI-R), a measure of the five-factor model of personality, Noordhof, Sellbom, Eigenhuis, and Kamphuis (2015) constructed an 18-item Demoralization subscale in a Dutch-speaking sample of patients attending a clinic for personality disorders in the Netherlands. In the current study we sought to cross-validate these findings in an English-speaking and diagnostically heterogeneous sample of psychiatric patients (N = 1930) receiving consultation or treatment at a large mental health and addiction center in Canada. Our results support the construct validity of the Demoralization subscale and its capacity to account for demoralization-related variance in the NEO PI-R. We believe these findings support the general tenets of demoralization and the presence of this construct in the NEO PI-R item pool. (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)0.002
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 score0.070
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.074
GPT teacher head0.442
Teacher spread0.368 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

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