Do risk assessment tools help manage and reduce risk of violence and reoffending? A systematic review.
Bibliographic record
Abstract
Although it is widely believed that risk assessment tools can help manage risk of violence and offending, it is unclear what evidence exists to support this view. As such, we conducted a systematic review and narrative synthesis. To identify studies, we searched 13 databases, reviewed reference lists, and contacted experts. Through this review, we identified 73 published and unpublished studies (N = 31,551 psychiatric patients and offenders, N = 10,002 professionals) that examined either professionals' risk management efforts following the use of a tool, or rates of violence or offending following the implementation of a tool. These studies included a variety of populations (e.g., adults, adolescents), tools, and study designs. The primary findings were as follows: (a) despite some promising findings, professionals do not consistently adhere to tools or apply them to guide their risk management efforts; (b) following the use of a tool, match to the risk principle is moderate and match to the needs principle is limited, as many needs remained unaddressed; (c) there is insufficient evidence to conclude that tools directly reduce violence or reoffending, as findings are mixed; and (d) tools appear to have a more beneficial impact on risk management when agencies use careful implementation procedures and provide staff with training and guidelines related to risk management. In sum, although risk assessment tools may be an important starting point, they do not guarantee effective treatment or risk management. However, certain strategies may bolster their utility. (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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".