This study employs machine learning algorithms to examine the causes for engaging in violent offending in individuals with schizophrenia spectrum disorders. Data were collected from 370 inpatients at the Centre for Inpatient Forensic Therapy, Zurich University Hospital of Psychiatry, Switzerland. Based on findings of the general strain theory and using logistic regression and machine learning algorithms, it was analyzed whether accumulation and type of stressors in the inpatients’ history influenced the severity of an offense. A higher number of stressors led to more violent offenses, and five types of stressors were identified as being highly influential regarding violent offenses. Our findings suggest that an accumulation of stressful experiences in the course of life and certain types of stressors might be particularly important in the development of violent offending in individuals suffering from schizophrenia spectrum disorders. A better understanding of risk factors that lead to violent offenses should be helpful for the development of preventive and therapeutic strategies for patients at risk and could thus potentially reduce the prevalence of violent offenses.

Purpose: This study aims to explore risk factors for direct coercive measures (seclusion, restraint, involuntary medication) in a high risk subpopulation of offender patients with schizophrenia spectrum disorders.Methods: Five hundred sixty nine potential predictor variables were explored in terms of their predictive power for coercion/no coercion in a set of 131 (36.6%) offender patients who experienced coercion and 227 who did not, using machine learning analysis. The dataset was split (70/30%) applying variable filtering, machine learning model building, and selection embedded in nested resampling approach in one subset. The best model was then selected, and the most important variables extracted on the second data subset.Results: In the final model the following variables identified coercion with a balanced accuracy of 73.28% and a predictive power (area under the curve, AUC) of 0.8468: threat of violence, (actual) violence toward others, the application of direct coercive measures during past psychiatric inpatient treatments, the positive and negative syndrome scales (PANSS) poor impulse control, uncooperativeness, and hostility and the total PANSSscore at admission, prescription of haloperidol during inpatient treatment, the daily cumulative olanzapine equivalent antipsychotic dosage at discharge, and the legal prognosis estimated by a team of licensed forensic psychiatrists.Conclusions: Results confirm prior findings, add detail on factors indicative for the use of direct coercion, and provide clarification on inconsistencies. Limitations, clinical relevance, and avenues for future research are discussed.

Background: Despite abundant evidence that emotional distress is frequent in cancer patients and associated with adverse health outcomes, distress screening rates and adequate referrals to psychological support programs among those in need are insufficient in many cancer centers. We therefore aimed to analyze patient-and treatment-related barriers to distress screening and referrals to psychological support as a mandatory component of best-practice cancer care. Method:In the present explorative study, latent class analysis was used to identify homogeneous subgroups among 4837 patients diagnosed with cancer between 2011 and 2019.Results: Four subgroups were identified. Patients with a mental disorder and psychopharmacology were least probable to be screened for distress. Together with patients aged 65 or older and male patients, they were also less likely to receive psychological support. Patients hospitalized for 28 days or longer were most likely to be both screened and to receive psychological support.Conclusions: Clinicians and researchers are recommended not neglect patients with mental disorders and psychopharmacological treatment as well as male and elderly patients when screening for distress and providing access to psychological support. K E Y W O R D Sdistress screening, latent class analysis, length of stay, psychiatric disorders, psycho-oncology, psychopharmacology 1 | BACKGROUND Psychological distress 1 has been reported in 30%-50% of cancer patients 2-7 and is persistent even in cancer survivors in 20%-40% of cases. 2,8 Such symptoms often remain unnoticed in primarily somatic treatment settings, 3,[9][10][11][12] even when pronounced psychopathology is present. 9,10,12 As a consequence, consensus-based treatment guidelines have recommended screening for distress in cancer patients as part of routine treatment. 13,14 The process of screening is recommended to facilitate access to psychosocial support to those in need. 15,16 Contrary to this, 25%-80% of patients with cancer do not receive screening. [17][18][19][20] Furthermore, even if identification of distressed patients with cancer is successful, this does not regularly result in referral to adequate psychological support. 9,16,21 A recent meta-analysis 22 and comprehensive review 23 both suggest patients with cancer and a pre-existing mental disorder are at particular risk to be neglected in terms of receiving psychological support. Patient

Prior research on Hodgins’ (2008) typology of offenders with schizophrenia spectrum disorders (SSD) has revealed inconsistencies in the number of subgroups and the operationalization of the concept. This study addressed these inconsistencies by applying latent class analysis (LCA) based on the most frequently explored variables in prior research. This novel case-centred methodology identified similarities and differences between the subjects contained in the sample instead of the variables explored. The LCA was performed on 71 variables taken from data on a previously unstudied sample of 370 case histories of offenders with SSD in a centre for inpatient forensic therapies in Switzerland. Results were compared with Hodgins’ theoretically postulated patient typologies and confirm three separate homogeneous classes of schizophrenic delinquents. Previous inconsistencies and differences in operationalizations of the typology of offenders with SDD to be found in the literature are discussed.

Background: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the application of a new statistical methodology better accommodating this data structure. The present study attempts to investigate factors contributing to long-term hospitalization of schizophrenic offenders referred to a Swiss forensic institution, using machine learning algorithms that are better suited than conventional methods to detect nonlinear dependencies between variables. Methods: In this retrospective file and registry study, multidisciplinary notes of 143 schizophrenic offenders were reviewed using a structured protocol on patients' characteristics, criminal and medical history and course of treatment. Via a forward selection procedure, the most influential factors for length of stay were preselected. Machine learning algorithms then identified the most efficient model for predicting length-of-stay. Results: Two factors have been identified as being particularly influential for a prolonged forensic hospital stay, both of which are related to aspects of the index offense, namely (attempted) homicide and the extent of the victim's injury. The results are discussed in light of previous research on this topic. Conclusions: In this study, length of stay was determined by legal considerations, but not by factors that can be influenced therapeutically. Results emphasize that forensic risk assessments should be based on different evaluation criteria and not merely on legal aspects.

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Background There is limited research with inconsistent findings on differences between female and male offender patients with a schizophrenia spectrum disorder (SSD), who behave aggressively towards others. This study aimed to analyse inhomogeneities in the dataset and to explore, if gender can account for those. Methods Latent class analysis was used to analyse a mixed forensic dataset consisting of 31 female and 329 male offender patients with SSD, who were accused or convicted of a criminal offence and were admitted to forensic psychiatric inpatient treatment between 1982 and 2016 in Switzerland. Results Two homogenous subgroups were identified among SSD symptoms and offence characteristics in forensic SSD patients that can be attributed to gender. Despite an overall less severe criminal and medical history, the female-dominated class was more likely to receive longer prison terms, similarly high antipsychotic dosages, and was less likely to benefit from inpatient treatment. Earlier findings were confirmed and extended in terms of socio-demographic variables, diseases and criminal history, comorbidities (including substance use), the types of offences committed in the past and as index offence, accountability assumed in court, punishment adjudicated, antipsychotic treatment received, and the development of symptoms during psychiatric inpatient treatment. Conclusions Female offender patients with schizophrenia might need a more tailored approach in prevention, assessment and treatment to diminish tendencies of inequity shown in this study.

Migrants diagnosed with schizophrenia are overrepresented in forensic-psychiatric clinics. A comprehensive characterization of this offender subgroup remains to be conducted. The present exploratory study aims at closing this research gap. In a sample of 370 inpatients with schizophrenia spectrum disorders who were detained in a Swiss forensic-psychiatric clinic, 653 different variables were analyzed to identify possible differences between native Europeans and non-European migrants. The exploratory data analysis was conducted by means of supervised machine learning. In order to minimize the multiple testing problem, the detected group differences were cross-validated by applying six different machine learning algorithms on the data set. Subsequently, the variables identified as most influential were used for machine learning algorithm building and evaluation. The combination of two childhood-related factors and three therapy-related factors allowed to differentiate native Europeans and non-European migrants with an accuracy of 74.5% and a predictive power of AUC = 0.75 (area under the curve). The AUC could not be enhanced by any of the investigated criminal history factors or psychiatric history factors. Overall, it was found that the migrant subgroup was quite similar to the rest of offender patients with schizophrenia, which may help to reduce the stigmatization of migrants in forensic-psychiatric clinics. Some of the predictor variables identified may serve as starting points for studies aimed at developing crime prevention approaches in the community setting and risk management strategies tailored to subgroups of offenders with schizophrenia.