Psychometric properties of the Spanish version of the Psychopathy Checklist: Youth Version
Bibliographic record
Abstract
Abstract The current study examined the psychometric properties (factor structure, reliability and validity) of the Psychopathy Checklist: Youth Version (PCL:YV; Forth et al., 2003) in Spanish samples of male justice-involved youths between 15 and 22-years old. The PCL:YV was administered to two groups of youths who were incarcerated (n = 62; n = 95) and a sample of youth on probation (n = 122). Confirmatory factor analyses showed acceptable-to-good fit for three- and four-factor models. The four-factor hierarchical model with a second-order higher factor representing the whole psychopathy construct was considered for further analyses. PCL:YV scores showed high internal consistency and inter-rater reliability. Low-to-moderate convergence with other measures of psychopathic traits evinced an adequate convergent validity. Convergent and discriminant validity of the PCL:YV total scores were also confirmed considering several measures of psychopathology and personality traits. Importantly, the differential external correlates of the PCL:YV factors provide support for a multidimensional conceptualization of the psychopathy construct. Altogether, these results reveal adequate psychometric properties of the PCL:YV in Spanish population of justice-involved youths and justifies its use to assess psychopathic traits in this kind of populations.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".