Violence Risk Assessment Practices in Denmark: A Multidisciplinary National Survey
Bibliographic record
Abstract
Abstract With a quadrupling of forensic psychiatric patients in Denmark over the past 20 years, focus on violence risk assessment practices across the country has increased. However, information is lacking regarding Danish risk assessment practice across professional disciplines and clinical settings; little is known about how violence risk assessments are conducted, which instruments are used for what purposes, and how mental health professionals rate their utility and costs. As part of a global survey exploring the application of violence risk assessment across 44 countries, the current study investigated Danish practice across several professional disciplines and settings in which forensic and high-risk mental health patients are assessed and treated. In total, 125 mental health professionals across the country completed the survey. The five instruments that respondents reported most commonly using for risk assessment, risk management planning and risk monitoring were Broset, HCR-20, the START, the PCL-R, and the PCL:SV. Whereas the HCR-20 was rated highest in usefulness for risk assessment, the START was rated most useful for risk management and risk monitoring. No significant differences in utility were observed across professional groups. Unstructured clinical judgments were reported to be faster but more expensive to conduct than using a risk assessment instrument. Implications for clinical practice 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".