Predictive Validity of the Psychopathy Checklist: Screening Version for Intramural Behaviour in Violent Offenders—A Prospective Study at a Secure Psychiatric Hospital in Germany
Bibliographic record
Abstract
OBJECTIVE: To consider the extent to which the presence of psychopathy, as indicated by the psychopathy checklist: screening version (PCL:SV), can predict intramural behaviour in offenders with mental disorders serving compulsory treatment at a German forensic psychiatric hospital. METHOD: The PCL:SV was used with 48 offenders detained at a forensic psychiatric hospital in Germany. In a prospective design, objective and subjective measures of behaviour were compared for those identified as high and low scorers on the PCL:SV. Data were obtained from hospital records of disciplinary incidents (objective) and from interviews with case managers and therapists (subjective), according to predefined criteria and in standardized forms. RESULTS: The hospital records of the high scorers indicated they had been involved in significantly more disciplinary incidents than low scorers. Their behaviour was also rated significantly more negative by therapists than the low scorers. CONCLUSION: Numeorus studies found the psychopathy checklist (PCL) score to be a reliable predictor of recidivism in offenders after release. The present study has demonstrated that the PCL score has also predictive validity for intramural behaviour problems in individuals serving compulsory treatment at a forensic psychiatric hospital. As a result, we recommend the routine use of the PCL with offenders starting a period of compulsory detention to identify those at increased risk for problem behaviour.
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 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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.001 |
| 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".