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Record W4200144223 · doi:10.1037/lhb0000461

Validation and measurement invariance of the Inventory of Callous-Unemotional Traits in Chinese incarcerated and normative samples.

2021· article· en· W4200144223 on OpenAlexaff
Yao Zheng, Jieting Zhang, Lili Huang, Natalie Goulter

Bibliographic record

VenueLaw and Human Behavior · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser UniversityUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsMeasurement invariancePsychologyNormativePsycINFOPsychosocialClinical psychologySample (material)External validityConfirmatory factor analysisIncremental validityDevelopmental psychologyPsychometricsTest validitySocial psychologyPsychiatryStructural equation modelingMEDLINEStatistics

Abstract

fetched live from OpenAlex

OBJECTIVE: This study examined the factor structure and psychometric properties of the Inventory of Callous-Unemotional Traits (ICU) in a large sample of incarcerated Chinese male inmates and its measurement invariance with an independent normative sample of Chinese adults. We further investigated the external validity of the ICU in the incarcerated sample. HYPOTHESES: We hypothesized that the short forms of ICU would (a) provide a better model fit and greater internal consistency than the original 24-item ICU, (b) demonstrate measurement invariance between incarcerated and normative samples, and (c) show satisfactory external validity with a variety of external criterion measures. METHOD: A sample of incarcerated Chinese men (N = 498; M age = 33.14) and a normative sample of Chinese adults (N = 168; M age = 23.45, 41% male) self-reported on the ICU. The incarcerated sample also self-reported multiple psychosocial external measures. RESULTS: A short form of the ICU containing 11 items with two correlated factors (Callousness and Uncaring) demonstrated superior model fit, internal consistency, scalar invariance across the two samples, and external validity. CONCLUSIONS: Rigorously and accurately measuring callous-unemotional (CU) traits in Asian populations will enable future research to further understand how these traits develop over the life span, as well as their clinical and forensic implications. (PsycInfo Database Record (c) 2021 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.380

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)0.0000.000

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 head0.053
GPT teacher head0.309
Teacher spread0.256 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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