The safe pilot study: A prospective naturalistic study with repeated measures design to test protective factors against violence in and after discharge from forensic facilities
Bibliographic record
Abstract
Assessing violence risk amongst forensic patients is a vital legal and clinical task. The field of violence risk assessment has developed considerably over the past two decades but remains primarily risk focused. Despite this, growing attention to and work on protective factors or strengths has occurred. In this prospective naturalistic study with repeated observer-rated measures of 27 forensic patients, we tested the role of three potentially important but understudied dynamic protective factors: hope, insight, and resilience, along with a history of criminality, in terms of their impact on violence. Main effects models indicated that higher hopelessness and past criminal convictions were predictive of violence acts; higher resilience was associated with lower violence. In interaction models, hopelessness remained predictive. Importantly, there were significant interactions between resilience and past criminal convictions, with higher levels of resilience leading to lower violence, most amongst those with criminal convictions, and between resilience and hopelessness related emotional distress, in that higher resilience at high levels of patient acknowledged emotional distress due to hopelessness led to lower violence. Findings indicate the importance of focusing on strengths or protective factors in the assessment of risk and treatment planning for forensic patients. Despite the small sample, the repeated measures design was feasible and informative.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".