The characteristics of patients admitted to a forensic psychiatric intensive care unit (FPICU) in Belgium
Bibliographic record
Abstract
Background: Psychiatric intensive care units (PICUs) are increasingly recognised as essential components of forensic care for managing patients who are difficult to treat in conventional units. Despite this, there is limited research on the psychiatric and violence risk profiles of these patients, particularly in forensic settings. Aim: To identify the characteristics of patients admitted to a forensic PICU (FPICU) in Belgium. Method: A comparative analysis conducted between 2016 and 2022 on a cohort of 344 patients; 176 FPICU admissions and 168 forensic cases not admitted to the FPICU (NFPICU). Demographic, clinical and criminological profiles were assessed using the PCL-R, VRAG and HCR-20 tools. Results: As expected, FPICU patients demonstrated complex diagnostic profiles, including higher rates of substance use (52.8%), psychotic disorders (55.1%), and antisocial personality disorders with psychopathy (25.0%). Comorbid mental disorders were prevalent (69.9%), exacerbating their elevated risk of violence as assessed by the HCR-20 and VRAG. They were also more frequently involved in non-sexual violent (56.5%) and non-violent offences (68.7%). Coercive measures, including involuntary treatments (65.9%), seclusion (91.5%), and restraint (43.8%), were more commonly employed for FPICU patients. Conclusion: Patients admitted to the FPICU present complex psychiatric and criminological profiles, with high levels of comorbidity and violence risk. Specialised care strategies should be implemented in secure environments that emphasise therapeutic relationships to reduce restrictions, manage disruptive behaviours, and enhance treatment adherence. The implementation of the Forensic High and Intensive Care model, as developed in German-speaking countries, could support the reintegration of these patients into standard care units.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".