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Record W4323653221 · doi:10.1037/pas0001230

Comparing behavioral and psychological symptom structures on the Neuropsychiatric Inventory Questionnaire.

2023· article· en· W4323653221 on OpenAlexaff
David Andrés González, Zachary J. Resch, Maximilian A Obolsky, Jason R. Soble

Bibliographic record

VenuePsychological Assessment · 2023
Typearticle
Languageen
FieldMedicine
TopicDiverse Approaches in Healthcare and Education Studies
Canadian institutionsCentre for Movement Disorders
FundersNational Institute on AgingNational Institutes of Health
KeywordsPsycINFOPsychologyClinical psychologyEquivalence (formal languages)Confirmatory factor analysisPsychometricsMeasurement invarianceDementiaCognitionStructural equation modelingTest validityFactor analysisReliability (semiconductor)Developmental psychologyPsychiatryDiseaseMEDLINEStatisticsMedicine

Abstract

fetched live from OpenAlex

= 15.1) that was divided into exploratory, derivation, and holdover subsets for cross-validation. We found that a four-factor model had the best fit, with adequate reliability estimates, adequate τ-equivalence, and the least amount of measurement variance. Strict invariance across stage and syndrome was not supported, although there was adequate support for weaker restrictions (e.g., equal forms). Furthermore, all bifactor models had a significant increase in fit. In sum, the present study provides practical guidance on using NPI-Q factor-derived subscales and theoretical elaboration of BPSD's hierarchical and syndrome-variant structure. (PsycInfo Database Record (c) 2023 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.330
GPT teacher head0.483
Teacher spread0.153 · 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 source (direct Gemma or distilled Codex), 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 routes1
Has abstractyes

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