From risky individuals to risky systems: a conceptual framework for the next generation of forensic mental health research
Bibliographic record
Abstract
Risk in forensic mental health is often shaped as an individual issue. But what if risk is also produced by the systems meant to provide care and safety? This paper introduces the Risk in Systems Framework , a conceptual model to understand how risk emerges not just within people, but also through institutional practices and structural inequalities. Drawing on established models from criminology, psychology, and public health, the framework explores risk across three levels: the individual, the system, and the broader social structures. It helps identify how policies, professional norms, and historical legacies can shape who is labelled risky and what responses are considered appropriate. By shifting the focus from “risky individuals” to “risky systems,” this approach supports more responsive care for people in forensic mental health settings.
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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 | 0.026 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.007 | 0.073 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".