The Role of Psychopathic Personality Disorder in Violence Risk Assessments Using the HCR-20
Bibliographic record
Abstract
Antisocial and psychopathic traits are essential to evaluate when assessing risk for violence using the HCR-20. The role of the PCL-R on the HCR-20 was investigated using a series of meta-analytic tests. Across 34 samples in which both tools were rated, AUCs for violence were similar (∼.69), and exclusion of the psychopathy item (H7) did not reduce the HCR-20's accuracy. Quantitative synthesis of results from multivariate analyses conducted in 7 raw datasets that used both tools demonstrated that the average probability of observing violence for every point increase on the HCR-20 (without H7), while controlling for the PCL-R, was 23%, whereas for the PCL-R it was -1%. The HCR-20 (without H7) added incremental validity to the PCL-R, whereas the converse was not true, and only the HCR-20 (without H7) possessed unique predictive validity. Results suggest the HCR-20's predictive validity was not negatively impacted by excluding the PCL-R. Areas for future study are discussed, including research on various ways to assess and incorporate into risk assessment personality traits related to violence.
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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 | 0.109 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".