Validation and measurement invariance of the Inventory of Callous-Unemotional Traits in Chinese incarcerated and normative samples.
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".