Violence Risk Assessment Tools Used in Forensic and Acute Psychiatry in North America: A Scoping Review
Bibliographic record
Abstract
Violence in psychiatric settings presents a significant risk to patients, staff, and society at large. With over 400 risk assessment tools available globally, their applications and the risks they assess vary, allowing for diverse use in different situations. This scoping review investigated the risk management tools utilized in North America’s forensic psychiatry and acute psychiatric units, aiming to identify which ones are mainly used. A comprehensive search was conducted across PubMed, Embase, and PsycINFO databases, following PRISMA Guidelines, covering the literature from their inception date until 2023. Criteria for study inclusion required a focus on risk management tool use in forensic or acute psychiatric settings, originality (original studies, case reports, or systematic reviews), and a North American context. Out of 3059 identified studies, 40 were thoroughly analyzed. Commonly used risk assessment scales include HARM-FV, eHARM-FV, HCR-20, PCL-R, START, BVC, and DASA, with their reliability varying by the clinical context and the assessed population. The review highlights the heterogeneous application of static and dynamic scales across clinical settings, underscoring a need for more precise tools to improve risk assessments in forensic psychiatry, signaling a call for the development and validation of more sophisticated assessment instruments.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".