Assessing risk for aggression in forensic psychiatric inpatients: An examination of five measures.
Bibliographic record
Abstract
The present study examined risk for inpatient aggression, including treatment-related changes in risk, using a battery of 5 forensic instruments. The relative contributions of different types of risk factors to the assessment of risk for inpatient outcomes were also assessed. The Historical-Clinical-Risk Management-20V3, Short-Term Assessment of Risk and Treatability, Violence Risk Scale, Violence Risk Appraisal Guide-Revised, and Psychopathy Checklist-Revised were rated from archival information sources on a sample of 99 adult forensic inpatients from a Canadian psychiatric hospital. Pretreatment and posttreatment ratings were obtained on all dynamic study measures; associations between risk and change ratings with inpatient aggression were examined. Significant pretreatment-posttreatment differences were found on the HCR-20V3, START, and VRS; pretreatment scores on these measures each demonstrated predictive accuracy for inpatient aggression (AUC = .68 to .76) whereas the PCL-R and VRAG-R did not. HCR-20V3, VRS, and START dynamic scores demonstrated incremental predictive validity for inpatient aggression to varying degrees after controlling for static risk factors. Dynamic change scores from these 3 measures also demonstrated incremental concurrent associations with reductions in inpatient aggression after controlling for baseline risk. Several instruments demonstrated predictive validity for inpatient aggression and clinical/dynamic risk and change scores had unique associations with this outcome. The present findings suggest that risk assessments using the HCR-20 V3, START, and VRS may inform the management and reduction of inpatient aggression, as well as assessments of dynamic risk more generally. (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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.000 | 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".