Dataset Information

Serious juvenile offenders: classification into subgroups based on static and dynamic charateristics.

ABSTRACT: The population in juvenile justice institutions is heterogeneous, as juveniles display a large variety of individual, psychological and social problems. This variety of risk factors and personal characteristics complicates treatment planning. Insight into subgroups and specific profiles of problems in serious juvenile offenders is helpful in identifying important treatment indicators for each subgroup of serious juvenile offenders.To identify subgroups with combined offender characteristics, cluster-analyses were performed on data of 2010 adolescents from all juvenile justice institutions in the Netherlands. The study included a wide spectrum of static and dynamic offender characteristics and was a replication of a previous study, in order to replicate and validate the identified subgroups. To identify the subgroups that are most useful in clinical practice, different numbers of subgroup-solutions were presented to clinicians.Combining both good statistical fit and clinical relevance resulted in seven subgroups. Most subgroups resemble the subgroups found in the previous study and one extra subgroups was identified. Subgroups were named after their own identifying characteristics: (1) sexual problems, (2) antisocial identity and mental health problems, (3) lack of empathy and conscience, (4) flat profile, (5) family problems, (6) substance use problems, and (7) sexual, cognitive and social problems.Subgroups of offenders as identified seem rather stable. Therefore risk factor scores can help to identify characteristics of serious juvenile offenders, which can be used in clinical practice to adjust treatment to the specific risk and needs of each subgroup.

SUBMITTER: Hillege SL

PROVIDER: S-EPMC5740506 | biostudies-other | 2017

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Serious juvenile offenders: classification into subgroups based on static and dynamic charateristics.

Hillege Sanne L SL Brand Eddy F J M EFJM Mulder Eva A EA Vermeiren Robert R J M RRJM van Domburgh Lieke L

Child and adolescent psychiatry and mental health 20171222

<h4>Background</h4>The population in juvenile justice institutions is heterogeneous, as juveniles display a large variety of individual, psychological and social problems. This variety of risk factors and personal characteristics complicates treatment planning. Insight into subgroups and specific profiles of problems in serious juvenile offenders is helpful in identifying important treatment indicators for each subgroup of serious juvenile offenders.<h4>Methods</h4>To identify subgroups with com ...[more]

PMID: 29296120

Similar Datasets

Project description:BackgroundPrevious research has shown that chronic disease case definitions constructed using population-based administrative health data may have low accuracy for ascertaining cases of episodic diseases such as rheumatoid arthritis, which are characterized by periods of good health followed by periods of illness. No studies have considered a dynamic approach that uses statistical (i.e., probability) models for repeated measures data to classify individuals into disease, non-disease, and indeterminate categories as an alternative to deterministic (i.e., non-probability) methods that use summary data for case ascertainment. The research objectives were to validate a model-based dynamic classification approach for ascertaining cases of juvenile arthritis (JA) from administrative data, and compare its performance with a deterministic approach for case ascertainment.MethodsThe study cohort was comprised of JA cases and non-JA controls 16 years or younger identified from a pediatric clinical registry in the Canadian province of Manitoba and born between 1980 and 2002. Registry data were linked to hospital records and physician billing claims up to 2018. Longitudinal discriminant analysis (LoDA) models and dynamic classification were applied to annual healthcare utilization measures. The deterministic case definition was based on JA diagnoses in healthcare use data anytime between birth and age 16 years; it required one hospitalization ever or two physician visits. Case definitions based on model-based dynamic classification and deterministic approaches were assessed on sensitivity, specificity, and positive and negative predictive values (PPV, NPV). Mean time to classification was also measured for the former.ResultsThe cohort included 797 individuals; 386 (48.4 %) were JA cases. A model-based dynamic classification approach using an annual measure of any JA-related healthcare contact had sensitivity = 0.70 and PPV = 0.82. Mean classification time was 9.21 years. The deterministic case definition had sensitivity = 0.91 and PPV = 0.92.ConclusionsA model-based dynamic classification approach had lower accuracy for ascertaining JA cases than a deterministic approach. However, the dynamic approach required a shorter duration of time to produce a case definition with acceptable PPV. The choice of methods to construct case definitions and their performance may depend on the characteristics of the chronic disease under investigation.

Project description:Individuals with history of childhood trauma are characterized by aberrant resting-state limbic and paralimbic functional network connectivity. However, it is unclear whether specific subtypes of trauma (i.e., experienced vs observed or community) showcase differential effects. This study examined whether subtypes of childhood trauma (assessed via the Trauma Checklist [TCL] 2.0) were associated with aberrant intra-network amplitude of fluctuations and connectivity (i.e., functional coherence within a network), and inter-network connectivity across resting-state networks among incarcerated juvenile males (n = 179). Subtypes of trauma were established via principal component analysis of the TCL 2.0 and resting-state networks were identified by applying group independent component analysis to resting-state fMRI scans. We tested the association of subtypes of childhood trauma (i.e., TCL Factor 1 measuring experienced trauma and TCL Factor 2 assessing community trauma), and TCL Total scores to the aforementioned functional connectivity measures. TCL Factor 2 scores were associated with increased high-frequency fluctuations and increased intra-network connectivity in cognitive control, auditory, and sensorimotor networks, occurring primarily in paralimbic regions. TCL Total scores exhibited similar neurobiological patterns to TCL Factor 2 scores (with the addition of aberrant intra-network connectivity in visual networks), and no significant associations were found for TCL Factor 1. Consistent with previous analyses of community samples, our results suggest that childhood trauma among incarcerated juvenile males is associated with aberrant intra-network amplitude of fluctuations and connectivity across multiple networks including predominately paralimbic regions. Our results highlight the importance of accounting for traumatic loss, observed trauma, and community trauma in assessing neurobiological aberrances associated with adverse experiences in childhood, as well as the value of trained-rater trauma assessments compared to self-report.

Dataset Information

Serious juvenile offenders: classification into subgroups based on static and dynamic charateristics.

Publications

Serious juvenile offenders: classification into subgroups based on static and dynamic charateristics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets