The Usefulness of Using Static Assessments to Predict Levels of Risk in Offenders - Term Paper Example

Critically discuss the usefulness of using static assessments to predict levels of risk in offenders NAME: UNIVERSITY: COURSE: INSTRUCTOR: DATE: © 2012 Introduction In an effort to create a haven for everyone in the society, risk assessments have become a commonplace undertaking for almost all forms of governments. In the views of Hollin (2004), risk assessments are simply the predictions of the future behaviours in individuals living together. The fact that these assessments are mere predictions, Hollin (2004) argues that they are quite erroneous and frequently present profound implications both to suspected offenders and the society at large. Through the application of risk assessment, a suspected offender can either walk into freedom or get convicted. Through the same fate, the society can also risk the wrath of a potentially dangerous individual roaming free in their midst. To offset the occurrence of either eventuality, it has become imperative to initiate very reliable assessment methods that are devoid of error and biasness. Two such methods, static and dynamic assessments have become very fundamental. Static assessments on one hand use unchanging factors to predict an individual’s probability of re-offending (Canter and Youngs, 2009), while dynamic assessments base their predictions on factors that constantly change with time. The usefulness of these assessments alongside others is the basis of discussion in this report. Static versus Dynamic risk assessment methods As mentioned in the foregoing paragraph, static risk assessments apply unchanging factors to predict the probability of re-offending in an offender soon after being released from a correctional facility. Some of these factors include gender, age, academic achievements and mental state as identified by Harper and partner (2005). Hodgins and friend (2001) and Holloway and associates (2005) maintain that offenders who have had a conviction in the past or have suffered a mental health illness have a stronger probability of re-offending than those who have not. Similar arguments are proffered by Bartol and colleague (2008) and Canter (2008) who observe that violent offences are recurrent in individuals who have some criminal history; been involved in substance abuse or suffered from mental illness. In the same vein, sex offenders are known to re-offend if they’ve had an earlier sexual offence especially with small children (Van Wormer, 2010). It is further observed that sex offenders who recur in their offences are usually the ‘young, unmarried and belonging to antisocial backgrounds’ (Van Wormer, 2010). When these factors are considered wholesome, they are all found to be static and unchanging; and are thus acceptable in predicting an offender’s future behaviour. This is the reason why static risk assessments are considered the most reliable and valid among all other methods. Dynamic risk assessments similarly help to predict an offender’s probability of re-offending once released from a correctional facility. But unlike static risk assessments, dynamic risk assessments consider those changing factors that predispose an offender to re-offend immediately after winning freedom from a rehabilitation facility. As noted by Davies and partner (2012), common dynamic factors associated with general re-offending include ‘antisocial personality, social achievement, interpersonal conflict and substance abuse’. Numerous studies have presented a rich pool of information relating to offender recurrence to crime in connection to these changing factors. For instance, Holmes (2010) observes that poor social support, an antisocial lifestyle and a lax system toward sexual assault are factors that strongly influence re-offending in sex offenders. Likewise, Holloway et al (2005) found in their study that most dynamic factors such as antisocial history and treatment behaviour played a significant role in determining the release of a suspected offender from custody. Based on these studies therefore, Jackson (2008) advises that it is imperative to know all the dynamic factors surrounding an offender so as to predict his risk level. However, both static and dynamic risk assessment methods are measured by certain approaches outlined below. Actuarial risk assessment From the outset, actuarial risk assessment methods make use of inventoried data on the character of an offender to determine his risk level. This inventory may be obtained from institutional intake reports as well as case files as observed by Rettenberger and Hucker (2011). In the inventory, the offender’s personal details like academic and work history; cases of mental health problems as well as criminal activities are included. Research findings by Huss (2009) and Jackson (2008) illustrate that actuarial approaches are more consistent in risk assessment as opposed to other methods. But in spite of this realisation, actuarial risk assessments are found to have their own shortcomings. Firstly they fail to recognise the variations inherent among individuals when assessing risk. Secondly they over emphasise the use of static variables in their assessments as opposed to the consideration of clinically relevant variables as highlighted by Rettenberger and partner (2011). Even though actuarial risk assessment tools have been largely criticised in the past, their application is evident in some high profile institutions. For example, the US penal system is reportedly using actuarial risk assessments in predicting the risk levels of prisoners on parole before releasing them to the society (McMurran et al, 2009). The same have been observed in the UK parole board where actuarial risk assessment tools have yet again been widely used to calculate risk scores for use before releasing convicted prisoners (Huss, 2009). In addition to the foregoing applications, Hollin (2004) underscore the significance of these risk assessment tools in the insurance industry when calculating ‘probabilities of risks’ for various covers. Structured clinical risk assessment Structured clinical risk assessment on the other hand employs the combination of experience and professional proficiency. Rettenberger and friend (2011) argue that structured clinical risk assessment makes use of skillfully collected data using sound scientific techniques while allowing a lot of flexibility in the assessment process. Additionally, the method gives clinicians the autonomy to apply their professionalism in decision making. Just like the name suggests, structured clinical risk assessment originates from the medical field where clinical diagnosis of patients produces valuable data to be used in risk assessment. Rettenberger and associate (2011) indicate that structured clinical risk assessment process involves rigorous interviewing and observation of subjects in confinement. This exercise is conducted by a qualified clinician with the aim of building a profile of each individual slotted for release into the community. Research findings have variously demonstrated that predictions emanating from clinical risk assessments have been faced with a number of problems. Unreliability has been cited by Holmes (2010) as the most disturbing shortcoming plaguing clinical risk assessments. A part from this, Huss (2009) further holds that the absence of a common definition of ‘dangerousness’ and ‘base behaviours’ in offenders has greatly challenged clinicians in their predictions. Moreover, clinicians are found to have a limited ability in their judgment of individual offenders and have more than once delivered erroneous risk predictions in the past. Combined risk assessments After having witnessed the difficulties faced by each individual assessment tool in delivering accurate and reliable results, it has become fundamental to use combined risk assessments. The basic principle behind this approach is to combine actuarial calculations of probability with detailed clinical interviewing as espoused by Davies et al (2012). In this approach, static risk factors such as previous history of behaviours and convictions are combined with well selected dynamic factors. Proponents of this approach contend that there is a ‘value-added’ component because advantages of each individual method are exploited. All in all, the accuracy and consistency of risk assessments is enhanced by applying this approach as noted by Canter and Youngs (2009). Different scoring methods on each assessment There are different scoring methods resident in the area of risk assessment. Both actuarial and structured clinical risk assessment have developed scoring instruments that have emerged fundamental in accurate and reliable predictions. The Violent Risk Appraisal Guide (VRAG) for example is an actuarial scoring tool for predicting risk of violence for a given period of time especially for mentally disordered offenders upon their release (Hodgins and Muller-Isberner, 2001). Since the scale is developed in the medical arena, it simply consults clinical records in order to predict risk levels of individual offenders. SORAG on the other hand is yet another score that helps to predict risk violence levels in offenders previously convicted for sexual offences as demonstrated by Rettenberger and Hucker (2011). Similarly, structured clinical risk assessments apply specific scoring scales in assessing risk levels in certain instances. To assess the risk level of violent behaviour in matrimony, Canter (2008) has recommended the use of SARA particularly in men with a history of assaulting their wives. Canter (2008) purposely underlines the role played by this tool in gauging the recurrence of wife battery in suspected men. Other assessment tools used to score levels of risk in different categories of people include the SAVRY which predicts the probability of recurrence of violence in youth; WRA – 20 and ERA – 20 both measuring violence risk in the workplace (Bartol and Bartol, 2008). Sexual assessments in analysing the risk in offenders Van Wormer (2010) has identified two basic methods for predicting sex offence recurrence in offenders. These methods include the RRASOR which is a tool scoring for re-offending in sexual offences and the ‘Structured Anchored Clinical Judgement’ (SACJ) predicting violence risk in sex offenders. The two methods are lauded for accurate prediction and have been combined into a single tool known as STATIC 99. Van Wormer (2010) explains that this tool has contributed so much towards the improvement of predictive accuracy for re-offending in recent times. In addition to STATIC 99 (revised to STATIC – 2000), another actuarial tool called the SONAR has been developed to assess risk for sex offenders (Van Wormer, 2010). The SONAR measures specifically the change in risk levels throughout the course of an offender’s sentence by focusing on dynamic factors related to recurrence. The scale considers five stable factors, including intimacy deficits, social influences, tolerance toward sexual offending, and sexual and general self-regulation as highlighted by Van Wormer (2010). Limitations for different types of assessments From the foregoing discourse, it is clear that the three methods discussed above have been applicable in various setups and at different situations. Not all of them have travelled the smooth road throughout but have constantly witnessed the bumpy road as noted by Jackson (2008). Firstly, Howitt (2002) underlines the problem of unreliability and validity presented by the various risk assessment tools, particularly outside the centres in which they were developed. A part from actuarial risk assessments that post some degree of reliability as identified throughout this discourse, a lot needs to be done to the other tools in application so as to improve on their reliability level. Moreover, the state of literature on accuracy of violence risk is currently deplorable and a lot of research work needs to be conducted in order to improve current violence prediction accuracy (Rettenberger and Hucker, 2011). The lack of uniformity in the statistical procedures used to assess predictive accuracy, and the variation in choice of cut-off scores on risk prediction tools is yet another limitation. It has been discovered that this usually make comparisons between studies very difficult. Finally, the reporting of receiver operator characteristic data should improve the current situation as espoused by Davies and Beech (2012). Conclusion Static and dynamic risk assessment methods have been known to belong to two broad categories referred to as actuarial and clinical assessment methods respectively. Static risk assessment methods belonging to actuarial methods tend to be more reliable and valid as compared to dynamic methods which reside in structured clinical methods. This is reiterated by Harper and Chitty (2005) who confirm that clinical methods are far much less accurate as compared to actuarial methods, which are actually very critical in predicting levels of risk in offenders. But while none of these two methods is good enough as a stand-alone tool, proponents advocate for the combination of both approaches so as to improve the quality of decision-making in risk assessment (Davies and Beech, 2012). References Bartol, C. R and Bartol, A. M (2008) Criminal behavior: A psychological approach, 9th ed. USA: Pearson Education. Canter, D. (2008). Criminal and Investigative Psychology. Oxford University Press Canter, D., and Youngs, A. (2009). Investigative psychology: Offender profiling and the analysis of criminal action. UK: Wiley & Sons. Davies, G., and Beech, A. (2012). Forensic psychology: Crime, justice, law, interventions. UK: Wiley & Sons Harper, G and Chitty, C. (2005). The impact of corrections on re-offending: A review of ‘what works’. (3rd Ed). London: Home Office. Hodgins, S., and Muller-Isberner, R. (2001). Violence, crime and mentally disordered offenders: Concepts and methods for effective treatment and prevention. Canada: Wiley & Sons. Hollin, C. (2004). The Essential Handbook of Offender Assessment and Treatment. Chichester: Wiley. Holloway, K et al (2005). Systematic review of criminal justice and treatments programmes in reducing drug-related crime. Home Office. Holmes, D. A. (2010). Abnormal, clinical & forensic psychology. UK: Pearson education. Howitt, D. (2002). Introduction to forensic & criminal psychology, 3rd ed. UK: Pearson Education Huss, M. T. (2009). Forensic psychology: Research, clinical practice, and applications.UK: Wiley & Sons. Jackson, R. (2008). Learning forensic assessment. UK: Routledge. McMurran, M., Khalifa, N, and Gibbon, S. (2009). Forensic mental health. USA: Willan. Rettenberger, M. and Hucker, S. J. (2011) “Structured professional guidelines: International applications”, International Perspectives on Sex Offender Assessment & Treatment: Theory, Practice and Research, Chapter 5, pp 85-110. Van Wormer, K. (2010). Working with female offenders: a gender sensitive approach. 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In the same vein, sex offenders are known to re-offend if they’ve had an earlier sexual offence especially with small children (Van Wormer, 2010). It is further observed that sex offenders who recur in their offences are usually the ‘young, unmarried and belonging to antisocial backgrounds’ (Van Wormer, 2010). When these factors are considered wholesome, they are all found to be static and unchanging; and are thus acceptable in predicting an offender’s future behaviour. This is the reason why static risk assessments are considered the most reliable and valid among all other methods.

Dynamic risk assessments similarly help to predict an offender’s probability of re-offending once released from a correctional facility. But unlike static risk assessments, dynamic risk assessments consider those changing factors that predispose an offender to re-offend immediately after winning freedom from a rehabilitation facility. As noted by Davies and partner (2012), common dynamic factors associated with general re-offending include ‘antisocial personality, social achievement, interpersonal conflict and substance abuse’.

Numerous studies have presented a rich pool of information relating to offender recurrence to crime in connection to these changing factors. For instance, Holmes (2010) observes that poor social support, an antisocial lifestyle and a lax system toward sexual assault are factors that strongly influence re-offending in sex offenders. Likewise, Holloway et al (2005) found in their study that most dynamic factors such as antisocial history and treatment behaviour played a significant role in determining the release of a suspected offender from custody.

Based on these studies therefore, Jackson (2008) advises that it is imperative to know all the dynamic factors surrounding an offender so as to predict his risk level. However, both static and dynamic risk assessment methods are measured by certain approaches outlined below. Actuarial risk assessment From the outset, actuarial risk assessment methods make use of inventoried data on the character of an offender to determine his risk level. This inventory may be obtained from institutional intake reports as well as case files as observed by Rettenberger and Hucker (2011).

In the inventory, the offender’s personal details like academic and work history; cases of mental health problems as well as criminal activities are included. Research findings by Huss (2009) and Jackson (2008) illustrate that actuarial approaches are more consistent in risk assessment as opposed to other methods. But in spite of this realisation, actuarial risk assessments are found to have their own shortcomings. Firstly they fail to recognise the variations inherent among individuals when assessing risk.

Secondly they over emphasise the use of static variables in their assessments as opposed to the consideration of clinically relevant variables as highlighted by Rettenberger and partner (2011). Even though actuarial risk assessment tools have been largely criticised in the past, their application is evident in some high profile institutions. For example, the US penal system is reportedly using actuarial risk assessments in predicting the risk levels of prisoners on parole before releasing them to the society (McMurran et al, 2009).

The same have been observed in the UK parole board where actuarial risk assessment tools have yet again been widely used to calculate risk scores for use before releasing convicted prisoners (Huss, 2009). In addition to the foregoing applications, Hollin (2004) underscore the significance of these risk assessment tools in the insurance industry when calculating ‘probabilities of risks’ for various covers. Structured clinical risk assessment Structured clinical risk assessment on the other hand employs the combination of experience and professional proficiency.

Rettenberger and friend (2011) argue that structured clinical risk assessment makes use of skillfully collected data using sound scientific techniques while allowing a lot of flexibility in the assessment process.

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