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Identifying multiple recidivists in a state hospital population
1Community Service, Rockland Psychiatric Center, Orangeburg, NY, USA.
Abstract:
Of 195 patients admitted to a state hospital over a four-month period, 74 were multiple recidivists. Nearly half of the 195 patients could be assigned to one of six profile subgroups of recidivists described in a previous study. Because of the high rate of medication noncompliance in this state hospital sample (65 percent), noncompliance alone was not an adequate predictor of multiple recidivism. The study found that membership in a profile subgroup coupled with factors related to gender, medication noncompliance, homelessness, and arrest history may be a more useful way of predicting multiple recidivism in samples of newly admitted patients.
Insights
Predicting multiple recidivism in state hospital patients requires more than just medication noncompliance. Combining patient profile subgroups with factors like gender, homelessness, and arrest history offers a more accurate prediction model.
Area of Science:
- Forensic Psychiatry
- Clinical Psychology
- Public Health
Background:
- Recidivism poses a significant challenge in state hospital systems, impacting resource allocation and public safety.
- Previous research identified six distinct profile subgroups among recidivist patients.
- High rates of medication noncompliance are common in state hospital populations.
Purpose of the Study:
- To investigate predictors of multiple recidivism in a sample of newly admitted state hospital patients.
- To determine if medication noncompliance alone is sufficient for predicting multiple recidivism.
- To explore the utility of combining patient profile subgroups with other demographic and clinical factors for enhanced prediction.
Main Methods:
- Analysis of 195 patients admitted to a state hospital over a four-month period.
- Classification of patients into previously defined recidivist profile subgroups.
- Assessment of medication noncompliance, gender, homelessness, and arrest history as potential predictors.
Main Results:
- 74 out of 195 patients (37.9%) were identified as multiple recidivists.
- Nearly half of the patients could be assigned to one of the six established recidivist profile subgroups.
- Medication noncompliance (65%) was prevalent but not a sole predictor of multiple recidivism.
- A combination of profile subgroup membership with gender, noncompliance, homelessness, and arrest history significantly improved prediction accuracy.
Conclusions:
- Medication noncompliance alone is insufficient for predicting multiple recidivism in this patient sample.
- Integrating patient profile subgroups with demographic and historical factors offers a more robust predictive model for multiple recidivism.
- This approach can aid in identifying high-risk individuals for targeted interventions within state hospital settings.