Longitudinal Network Structure and Changes of Clinical Risk and Protective Factors in a Nationwide Sample of Forensic Psychiatric Patients

• Published date: 01 November 2020
• Author: Marinus Spreen,Marija Jankovic,Stefan Bogaerts,Erik Masthoff
• DOI: 10.1177/0306624X20923256
• Date: 01 November 2020
• Subject Matter: Articles

https://doi.org/10.1177/0306624X20923256

International Journal of

Offender Therapy and

Comparative Criminology

2020, Vol. 64(15) 1533 –1550

© The Author(s) 2020

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DOI: 10.1177/0306624X20923256

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Article

Longitudinal Network

Structure and Changes of

Clinical Risk and Protective

Factors in a Nationwide

Sample of Forensic

Psychiatric Patients

Stefan Bogaerts1,2, Marinus Spreen3,

Erik Masthoff2, and Marija Jankovic1,2

Abstract

In this study, we investigated network configurations of 14 Clinical risk and protective

factors in a sample of 317 male forensic psychiatric patients across two time points:

at the time of admission to the forensic psychiatric centers (T1) and at the time of

unconditional release (T2). In terms of network structure, the strongest risk edge

was between “hostility–violation of terms” at T1, and between “hostility–impulsivity”

at T2. “Problem insight–crime responsibility” was the strongest protective edge, and

“impulsivity–coping skills” was the strongest between-cluster edge, at both time

points, respectively. In terms of strength centrality, “cooperation with treatment” had

the highest strength centrality at both measurement occasions. This study expands

the risk assessment field toward a better understanding of dynamic relationships

between individual clinical risk and protective factors and points to the highly central

risk and protective factors, which would be the best for future treatment targets.

Keywords

network analysis, clinical risk factors, clinical protective factors, the HKT-R, the risk–

need–responsivity model, the good lives model

1Tilburg University, the Netherlands

2Fivoor Science and Treatment Innovation (FARID), Rotterdam, the Netherlands

3NHL Stenden University of Applied Sciences, Leeuwarden, the Netherlands

Corresponding Author:

Stefan Bogaerts, Professor, Department of Developmental Psychology, Tilburg University, P.O. Box

90153, 5000 LE Tilburg, the Netherlands.

Email: S.Bogaerts@tilburguniversity.edu

923256IJOXXX10.1177/0306624X20923256International Journal of Offender Therapy and Comparative CriminologyBogaerts et al.

research-article2020

1534 International Journal of Offender Therapy and Comparative Criminology 64(15)

In the past decades, a large amount of research has been done on dynamic risk factors

(DRFs) that are related to criminal behavior and reoffending. Originally, supported by

general personality models, cognitive social learning perspectives, and the risk–need–

responsivity model (RNR: Andrews & Bonta, 2006; Andrews et al., 2012), eight risk

factors (Central Eight) were identified, which are prominent in the explanation and

prediction of criminal behavior, treatment outcome, and reoffending. These factors

were the Big Four risk factors (history of antisocial behavior, antisocial personality

pattern, antisocial cognition, and antisocial peers) and the Moderate Four risk factors

(family/marital condition, school/work, leisure/recreation, and substance abuse). The

Central Eight risk factors have played subsequently a crucial role in the development

of various risk assessment tools and are aimed to predict the likelihood that a prisoner

or forensic psychiatric patient will reoffend in the same offense or another one after

release. Furthermore, these tools are also used to investigate the treatment progress of

forensic patients during their stay in the institution and to estimate the likelihood of

future inpatient violence (Jeandarme et al., 2019). These predictions are based on his-

torical, clinical, and future factors, and predictions can be made at scale level and/or at

factor level.

Although the RNR model is currently a dominant treatment approach in the foren-

sic field, it has been recently criticized for overemphasizing offender risk factors and

not paying enough attention to protective factors of offenders (Ward et al., 2007). The

good lives model (GLM) has been therefore proposed as an alternative or addition to

the RNR approach. The GLM is a strength-based approach to the rehabilitation of

offenders that focuses primarily on increasing competencies and skills for offenders

to, indirectly, reduce the risk of reoffending (Höing et al., 2013). According to this

model, motivating offenders and creating a sound therapeutic alliance are key compo-

nents of effective treatment (Ward & Brown, 2004). The purpose of this study was to

get more insight into which factors, that is, risk, protective, or both, should be the

pivotal targets of treatment options in forensic psychiatric patients measured at the

moment of admission and unconditional release.

Furthermore, a major limitation of how risk and protective factors are used today in

research and clinical work is that only information is available about the linear inde-

pendent contribution of individual risk/protective factors or (sub)scales in the predic-

tion of the risk of recidivism (Fazel et al., 2012). Information about unidirectional or

bidirectional associations between single risk and protective factors, and whether

associations between factors can change over time, is currently hardly applied in

forensic psychiatry. Because high rates of comorbidities have been reported in foren-

sic psychiatric patients and are thought to contribute to reoffending (Black et al.,

2010), it is very important to gain insight into the interaction of risk and protective

factors, and changes in these interactions during inpatient forensic treatment.

Relying only on linear and independent associations of risk and protective factors,

and not considering reciprocal associations between risk and protective factors may

attenuate or mask significant information, which cannot be taken into account in the

treatment process (Beggs, 2010). Hence, it is important to study associations at the

item level and to explore how DRFs and protective factors are reciprocally associated