In this study, growth trajectories (from admission until unconditional release) of crime-related dynamic risk factors were investigated in a sample of Dutch forensic patients (N = 317), using latent growth curve modeling. After testing the unconditional model, three predictors were added: first-time offender versus recidivist, age, and treatment duration. Postanalyses were chi-square difference tests, t tests, and analyses of variance (ANOVAs) to assess differences in trajectories. Overall, on scale level, a decrease of risk factors over time was found. The predictors showed no significant slope differences although age and treatment duration differed significantly at some time points. The oldest age group performed worse, especially at later time points. Treatment duration effects were found at the second time point. Our results that forensic patients show a decrease in crime-related risk factors may indicate that treatment is effective. This study also found differences in growth rates, indicating the effect of individual differences
In this study, growth trajectories (from admission until unconditional release) of crime-related dynamic risk factors were investigated in a sample of Dutch forensic patients (N = 317), using latent growth curve modeling. After testing the unconditional model, three predictors were added: first-time offender versus recidivist, age, and treatment duration. Postanalyses were chi-square difference tests, t tests, and analyses of variance (ANOVAs) to assess differences in trajectories. Overall, on scale level, a decrease of risk factors over time was found. The predictors showed no significant slope differences although age and treatment duration differed significantly at some time points. The oldest age group performed worse, especially at later time points. Treatment duration effects were found at the second time point. Our results that forensic patients show a decrease in crime-related risk factors may indicate that treatment is effective. This study also found differences in growth rates, indicating the effect of individual differences
Aggressive incidents occur frequently in health care facilities, such as psychiatric care and forensic psychiatric hospitals. Previous research suggests that civil psychiatric inpatients may display more aggression than forensic inpatients. However, there is a lack of research comparing these groups on the incident severity, even though both frequency and severity of aggression influence the impact on staff members. The purpose of this study is to compare the frequency and severity of inpatient aggression caused by forensic and civil psychiatric inpatients in the same Dutch forensic psychiatric hospital. Data on aggressive incidents occurring between January 1, 2014, and December 31, 2017, were gathered from hospital files and analyzed using the Modified Overt Aggression Scale, including sexual aggression (MOAS+). Multilevel random intercept models were used to analyze differences between forensic and civil psychiatric patients in severity of aggressive incidents. In all, 3,603 aggressive incidents were recorded, caused by 344 different patients. Civil psychiatric patients caused more aggressive incidents than forensic patients and female patients caused more inpatient aggression compared with male patients. Female forensic patients were found to cause the most severe incidents, followed by female civil psychiatric patients. Male forensic patients caused the least severe incidents. The findings have important clinical implications, such as corroborating the need for an intensive treatment program for aggressive and disruptive civil psychiatric patients, as well as emphasizing the importance of gender-responsive treatment
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