Trajectories of Dynamic Risk Factors as Predictors of Violence and Criminality in Patients Discharged From Mental Health Services: A Longitudinal Study Using Growth Mixture Modeling
Bibliographic record
Abstract
Background: Individuals with severe mental illnesses are at greater risk of offences and violence, though the relationship remains unclear due to the interplay of static and dynamic risk factors. Static factors have generally been emphasized, leaving little room for temporal changes in risk. Hence, this longitudinal study aims to identify subgroups of psychiatric populations at risk of violence and criminality to reduce these behaviors, considering the dynamic changes of symptomatology and substance use. Method: A total of 825 patients from the MacArthur Violence Risk Assessment Study having completed 5 post-discharge follow-ups were analyzed. Individuals were classified into outcome trajectories (violence and criminality). Trajectories were computed for each substance (cannabis, alcohol and cocaine, alone or combined) and for symptomatology and inputted as dynamic factors, along with other demographic and psychiatric static factors, into binary logistic regressions for predicting violence and criminality. Best predictors were then identified using backward elimination and ROC curves were calculated for both models. Results: Two trajectories were found for violence (Low vs. High violence). Best predictors for belonging in the High-violence group were low verbal intelligence (baseline), higher psychopathy (baseline) and anger (mean) scores, persistant cannabis use (alone) and persistent moderate affective symptoms. The model’s AUC was 0.773. Two trajectories were also chosen as being optimal for criminality. The final model to predict High-criminality yielded to an AUC of 0.788, retaining as predictors male sex, lower educational level, higher psychopathy score (baseline), persistent polysubstance use (cannabis, cocaine and alcohol) and persistent cannabis use (alone). Both models were moderately predictive of outcomes. Conclusion: Static factors identified as predictors are consistent with previously published literature. Concerning dynamic factors, unexpectedly, cannabis alone was an independent co-occurring variable in the violence model, as well as affective symptoms. For criminality, our results are novel because a very there are very few studies on criminal behaviors in a psychiatric non-forensic population. In conclusion, these results emphasize the need to study more the impact of longitudinal patterns of specific substance use and high affective symptoms, and to evaluate more profoundly the predictors of crime, separately from violence.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".