<p>Violence after discharge from forensic units in the safe pilot study: a prospective study with matched pair design</p>
Bibliographic record
Abstract
OBJECTIVE: This paper reports on a prospective naturalistic study of violent recidivism after discharge from forensic mental health. Main aims were to find predictors of violence and to test the feasibility of a matched pair design for this purpose. METHODS: Patients from the Safe pilot project (n=18) and a group of controls (n=18) were matched on 10 variables, such as diagnosis, seriousness of violence, setting after discharge, and risk management plans. All the Safe pilot patients had been through repeated measurement of dynamic risk factors of violence the year before discharge to develop efficient risk management plans for use after discharge. We wanted to test whether violent recidivism during follow-up would be lower and less serious in the Safe pilot group. RESULTS: We found no significant between-group difference concerning number of patients with violent recidivism. However, the Safe pilot patients had significantly lower rates of violence and fewer severe violent episodes. In the control group, there was a significant association between a high number of risk management plans and high rates of violence. There was a statistical trend for the opposite association in the Safe pilot group. CONCLUSION: We discuss this in terms of a possible gap between the development and implementation of plans.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".