Psychometric properties and prognostic usefulness of the Youth Psychopathic Traits Inventory (YPI) as a component of a clinical protocol for detained youth: A multiethnic examination.
Bibliographic record
Abstract
Prior studies have shown that the Youth Psychopathic Traits Inventory (YPI) holds promise as a self-report tool for assessing psychopathic traits in detained adolescents. However, these studies have been conducted in a research context where anonymity and confidentiality are provided. Few studies have examined the usefulness of the YPI in clinical settings. To address this research gap, the present study examined data from 1,559 detained boys who completed the YPI as part of a clinical protocol. Official criminal records were available for a subsample (n = 848), allowing us to test the prognostic usefulness of the YPI. Results of confirmatory factor analyses, overall, support the proposed 3-factor structure, though model fit indices were not as good in Dutch boys compared to boys from other ethnic groups. Measurement invariance tests showed that the YPI scores are manifested in the same way across all 4 ethnic groups and suggest that means scores between the 4 ethnic groups are comparable. The YPI scores were internally consistent, and correlations with external variables, including aggression and conduct problems, support the convergent validity of the interpretation of YPI scores. Finally, results demonstrated that YPI scores were not significantly positively related to future criminality. In conclusion, this study suggests that the YPI may hold promise as a self-report tool for assessing psychopathic traits in detained male adolescents during a clinical protocol. However, the finding that the YPI did not predict future offending suggests that this tool should not yet be used for risk assessment purposes in forensic settings. (PsycINFO Database Record
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".