Risk and Protective Factors for Inpatient Aggression
Bibliographic record
Abstract
Dynamic risk and protective factors serve to assess the violence risk level of (forensic) psychiatric patients and offer guidance to clinical interventions. Risk assessment scores on Historical Clinical Risk Management–20 (HCR-20) risk factors and Structured Assessment of Protective Factors for violence risk (SAPROF) protective factors at different treatment stages were compared with violent incidents during treatment for 399 multidisciplinary coded assessments on 185 male and female forensic psychiatric patients. At later stages of treatment, less risk factors and more protective factors were observed, and predictive validities were higher. The HCR-20 and SAPROF scores showed good overall predictive validity for inpatient violence. The combination of risk factors and protective factors was a good predictor of incidents of aggressive behavior for different groups of patients, such as patients with violent or sexual offending histories, patients with major mental illnesses or personality disorders, and patients with a high score on psychopathy. Implications of these findings and recommendations for future research are discussed.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".