International Perspectives on the Practical Application of Violence Risk Assessment: A Global Survey of 44 Countries
Bibliographic record
Abstract
Mental health professionals are routinely called upon to assess the risk of violence presented by their patients. Prior surveys of risk assessment methods have been largely circumscribed to individual countries and have not compared the practices of different professional disciplines. Therefore, a Web-based survey was developed to examine methods of violence risk assessment across six continents, and to compare the perceived utility of these methods by psychologists, psychiatrists, and nurses. The survey was translated into nine languages and distributed to members of 59 national and international organizations. Surveys were completed by 2135 respondents from 44 countries. Respondents in all six continents reported using instruments to assess, manage, and monitor violence risk, with over half of risk assessments in the past 12 months conducted using such an instrument. Respondents in Asia and South America reported conducting fewer structured assessments, and psychologists reported using instruments more than psychiatrists or nurses. Feedback regarding outcomes was not common: respondents who conducted structured risk assessments reported receiving feedback on accuracy in under 40% of cases, and those who used instruments to develop management plans reported feedback on whether plans were implemented in under 50% of cases. When information on the latter was obtained, risk management plans were not implemented in over a third of cases. Results suggest that violence risk assessment is a global phenomenon, as is the use of instruments to assist in this task. Improved feedback following risk assessments and the development of risk management plans could improve the efficacy of health services.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".