The assessment of dynamic risk among forensic psychiatric patients transitioning to the community.
Bibliographic record
Abstract
Individuals with serious mental illness (SMI; i.e., psychotic or major mood disorders) are vulnerable to experiencing multiple forms of adverse safety events in community settings, including violence perpetration and victimization. This study investigates the predictive validity and clinical utility of modifiable risk factors for violence in a sample of 87 forensic psychiatric patients found Not Criminally Responsible on Account of Mental Disorder (NCRMD) transitioning to the community. Using a repeated-measures prospective design, we assessed theoretically based dynamic risk factors (e.g., insight, psychiatric symptoms, negative affect, treatment compliance) before hospital discharge, and at 1 and 6 months postdischarge. Adverse outcomes relevant to this population (e.g., violence, victimization, hospital readmission) were measured at each community follow-up, and at 12 months postdischarge. The base rate of violence (23%) was similar to prior studies of discharged psychiatric patients, but results also highlighted elevated rates of victimization (29%) and hospital readmission (28%) characterizing this sample. Many of the dynamic risk indicators exhibited significant change across time and this change was related to clinically relevant outcomes. Specifically, while controlling for baseline level of risk, fluctuations in dynamic risk factors predicted the likelihood of violence and hospital readmission most consistently (hazard ratios [HR] = 1.35-1.84). Results provide direct support for the utility of dynamic factors in the assessment of violence risk and other adverse community outcomes, and emphasize the importance of incorporating time-sensitive methodologies into predictive models examining dynamic risk. (PsycINFO Database Record
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".