Restorative Justice Practices in Forensic Mental Health Settings – A Scoping Review
Bibliographic record
Abstract
Restorative justice has long been considered an important alternative lens to approach illegal and harmful behavior compared to traditional criminal justice approaches. Despite this widespread and successful application, efforts to use this approach within forensic mental health settings have seemingly been minimal. This review aimed to synthesize the available information on the application, evidence for use, and barriers or unique considerations for restorative justice practices within forensic mental health settings. The PRISMA extension for scoping reviews checklist guided our reporting of the results. After conducting an extensive review of the literature, six peer-reviewed articles and five gray literature documents were included. Our results demonstrate that restorative justice approaches in forensic mental health settings are being used by a small number of committed individuals and are not broadly accepted or part of typical care services. The evidence for use of this approach is extremely sparse but do suggest that these interventions could be appropriate in forensic mental health settings as reports for positive impacts are available on three levels, with patients, victims, and organizations. Information about the unique considerations that should be made and how restorative justice in forensic mental health differs from use in other populations is 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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".