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Violence is a ‘leading worldwide public health problem’ (World Health Organisation, 2002; p. 1); the total annual cost of homicide and wounding in England and Wales has been estimated at £13billion (National Audit Office, 2008). In Brazil, the financial burden associated with violence accounted for 1.9% of GDP in 1996 and 1997 (World Health Organisation, 2002). A significant proportion of the costs associated with violence are incurred as a result of repeat offending, with evidence suggesting ‘less than 5% of the male population commits between 50% and 70% of all violent crimes’ (Hodgins, 2007, p. 12). The financial, social and psychological costs of violence and aggression have motivated considerable research and a vast amount of psychological research has been directed towards understanding aggression and developing methods for assessing, managing and treating violent offenders (Howells, Daffern, & Day, 2008). A proliferation of structured risk assessment methods and treatment programmes has emerged, though more recently the value of case formulation driven assessment and treatment has been recognized (Daffern & Howells, 2009; Daffern, Jones, & Shine, 2010; Hart, Sturmey, Logan, & McMurran, 2011). The offence paralleling behaviour (OPB) framework is a case formulation driven approach to offender assessment that has emerged as an adjunct to structured assessment and treatment methods. Originally coined by Jones (2004), Daffern, Jones, et al. (2007) have recently defined OPB as: ‘a behavioural sequence incorporating overt behaviours (that may be muted by environmental factors), appraisals, expectations, beliefs, affects, goals and behavioural scripts, all of which may be influenced by the patient's mental disorder, that is functionally similar to behavioural sequences involved in previous criminal acts’. (p. 267)
Although it is a novel term, OPB has its foundation in existing theoretical and conceptual frameworks (for review see Jones, 2010b); the notion of monitoring and modifying manifestations of persistent maladaptive behaviours as they arise in therapy resembles strategies used in functional analytic psychotherapy (Kohlenberg & Tsai, 1994) and schema therapy (Young, Klosko, & Weishaar, 2003). Within the forensic field, empirical evidence from research on sexual crimes (Grubin, Kelly, & Brunsdon, 2001) and burglary (Bennell & Canter, 2002) has consistently supported the view that if similar psychological components are activated then consistency in behaviour may occur across different situations and crimes (McDougall, Clark, & Fisher, 1994; Shoda, 1999), an assumption that provides the theoretical foundation for case- linkage analysis (Woodhams, Hollin, & Bull, 2007).
The OPB framework can be utilized by a range of clinical and custodial staff (Daffern et al., 2010) and has been seen as particularly promising within group-based in-custody treatment settings (e.g., custodial therapeutic communities), where behaviours emerging within custody but external to formal treatment sessions have historically been processed within psychotherapeutic groups. Engaging the multidisciplinary team to intervene as an offender enters a behavioural sequence that parallels their offending maximizes the potential for change. Additionally, OPBs can aid risk assessment as an adjunct to the nomothetic approach emphasized through many structured risk assessment instruments (Jones, 2010b). OPBs can also be used to guide intervention by identifying problem areas (i.e., those which maintain risk) and selecting the most appropriate type of intervention. This supports a person-centred approach to therapy and intervention. In addition, OPBs can be used in the monitoring and evaluation of treatment progress (Jones, 2010b). It is important to note here that despite considerable promise, the misapplication of the OPBs concept could be associated with detrimental consequences for clients. In the context of OPBs serving as an aid to the assessment of risk, misapplying the OPBs framework and interpreting non-relevant behaviours as OPBs could result in unduly restrictive risk management or intervention (Daffern et al., 2010).
OPB is a relatively new concept that remains in its developmental stage. There have however been several studies which have tested the OPBs concept in clinical settings (Daffern, Ferguson, Ogloff, Thomson, & Howells, 2007; Daffern, Howells, Stacey, Hogue, & Mooney, 2008; Daffern, Howells, Mannion, & Tonkin, 2009). This research has established that there is often some similarity between in-patient aggressive behaviours and patients' index offences (Daffern et al., 2009). However, many in-patient aggressive behaviours also lack similarity with index offences (Daffern et al., 2009) and some research has shown that sexually aggressive behaviour is not indicative of sexually abusive behaviour in custody (Daffern et al., 2008). Daffern, Ferguson, et al. (2007) did not find a relationship between in-patient aggressive behaviour and pre- and post-admission aggressive behaviour. However, they did find a relationship between pre- and post-admission aggression suggesting maintenance of a tendency towards aggression during hospitalization. It is possible that these tendencies were expressed in a topographically dissimilar manner, or that the prototypical precipitants to aggressive behaviour for this group were absent or that aggressive behaviour or its proxy (Gordon & Wong, 2010) were undetected during hospitalization (see Jones, 2010a). The difficulties in establishing reliable and valid OPBs formulations are consistent with the clinical case formulation literature (Hart et al., 2011), which shows limited reliability, validity and predictive power.
Concurrently, Jones (2010a) has developed a practice algorithm to assist staff with generating OPBs formulations (see Figure 1), which can then be used to guide treatment and assessment using the OPBs framework. According to Jones (2010a) the practice algorithm begins with the development of a reference formulation, which is based on a review of literature relevant to the offence and the individual case and ascertainment as to whether the information relevant to generic models and theory match the ‘case’ (see Jones, 2010a, pp. 71–88). The formulation can either be a ‘specific functional analysis/formulation of different individual offences’ or the ‘analysis of themes and common processes underpinning different offences’ (p. 72) peculiar to an offender. Once developed, this reference formulation is used to generate predictions of OPBs and Pro-social alternate behaviours (PABs; behavioural sequences manifesting in pro-social behaviour that otherwise resemble the OPB behavioural sequence) and to test whether these predictions come about during some follow-up period. Once the predictions are validated interventions are introduced to assist the client meet their needs in pro-social ways.
Figure 1. Practice algorithm for identifying offence paralleling behaviour. Reprinted from Jones, L. (2010a). Approaches to developing OPB formulations. In M. Daffern, L. Jones, & J. Shine (Eds.), Offence paralleling behaviour: A case formulation approach to offender assessment and intervention (pp. 71–88). UK: Wiley-Blackwell, with permission from John Wiley and Sons.
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Aims and Hypothesis
This study aimed to prospectively examine a novel method for developing OPBs formulations, based on Jones (2010a) practice algorithm, by examining the level of similarity between OPBs predictions derived from formulations based on previous behavioural repertoires, and actual in-patient aggressive behaviours in a Medium Secure Psychiatric Unit. PABs were conceptualized based upon anticipated pro-social behaviours that might be expected to satisfy the functional needs of the predicted OPBs. It was hypothesized that there would be similarity between predicted OPBs, derived from formulations based on previous offending and other aggressive acts, and actual observed aggressive behaviours. This would suggest that similar psychological components were active across previous and in-patient aggressive behaviours and that the formulations encouraged by Jones (2010a) algorithm are capable to identifying these OPBs and PABs.
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In total, 20 aggressive behaviours (the purported OPBs) and three positive behaviours (the purported PABs) were identified. All participants were aggressive on at least one occasion. The number of aggressive behaviours per participant ranged from 1 (n = 2) to 5 (n = 2). Behaviours for which there was insufficient information to score the different categories used to compare predictions and actual behaviours were excluded. Five (25%) aggressive behaviours were excluded for this reason. Therefore, the final analysis included 15 actual aggressive behaviours and three positive behaviours.
A Jaccard's coefficient was derived for each comparison between a prediction and a follow-up behaviour. Table 1 displays the minimum and maximum Jaccard's coefficients derived from the analysis per participant for aggressive behaviours. In addition, similarity coefficients were derived for actual PABs and their corresponding predictions (see Table 1).
Table 1. Minimum and maximum Jaccard's coefficients per participant
|Participant||Minimum–maximum aggressive behaviours||Minimum–maximum positive behaviours|
In order to establish a baseline level of similarity, predictions were also randomly paired with actual aggressive behaviours. Table 2 displays the minimum and maximum similarity coefficients for 15 randomly paired predictions and actual aggressive behaviours.
Table 2. Minimum and Maximum Jaccard's coefficients for randomly paired aggressive behaviours and OPB predictions
|Participant||Minimum and Maximum Jaccard's coefficients|
The Jaccard's coefficients of matched pairs were compared to the Jaccard's coefficients of random pairs. The Jaccard's scores for matched pairs were not normally distributed (matched D(15) = .25, p < .05 and random D(15) = .15, p > .05). Therefore, Wilcoxon signed-rank test was used to determine whether there was a statistically significant difference between the matched and paired Jaccard's scores. A statistically significant difference was found between the Jaccard's scores of the matched and random pairs; matched pairs (Median = .857) had a significantly higher similarity coefficient compared to random pairs (Median = .286), z = −3.108, p < .01).
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This study prospectively tested the OPB framework using predictions of OPBs and PABs derived from a novel practice algorithm (Jones, 2010a). Results revealed greater similarity between OPBs predictions derived from structured analysis of previous aggressive acts and actual incidents of inpatient aggression than randomly paired predictions and inpatient aggressive behaviours. The similarity of predicted and actual behaviour was significantly higher than what would be expected by chance. This result provides some validation for the OPBs framework by showing that predictions of OPBs based on careful analysis of an individual's history of violent behaviour can be used to predict inpatient aggressive behaviours.
The current findings differ somewhat from previous research conducted by Daffern et al. (2009) who did not find a significant difference between matched and random pairs of index offences and inpatient aggressive behaviour. Other studies (Daffern, Ferguson, et al., 2007, 2008) have also reported no relationship between pre-admission and in-patient aggression and between sexually abusive behaviour during hospitalization and sexual offence history.
There are several significant differences between the present study and previous research, which provide useful insights into the development and use of the OPBs framework. Firstly, Daffern et al. (2009) examined similarity between an index offence and in-patient aggressive behaviours whereas the current study considered all previous behaviours that might be relevant to the individual's violent offending cycle. By examining various offences and relevant behaviours the dominant themes within an individual's offending can be elucidated; analysing a discrete offence may not allow for persistent themes to be identified, resulting in invalid OPBs predictions. The current study's approach also allowed developmental sequences (Jones, 2004) to be identified. The lack of similarity between matched and random pairs in Daffern et al. (2009) study and the results of the present study suggest that a careful analysis of an individual's full offending and personal history must be taken into account when developing OPBs formulations and making OPBs predictions; an OPBs analysis should not be completed on an index offence alone.
In addition, this study utilized a wider variety of situational and dispositional factors to measure similarity than has been used previously. This makes the OPBs formulation process more robust and less prone to error or bias. It is also important to highlight that the present study used similar behavioural facets as the Daffern et al. (2009) study, although there were substantial differences in the protocol with regard to how information was extracted and recorded from case notes to enhance the coding of these nine characteristics. Specifically, this study used a variety of bottom-up (information derived from clinical notes and past Behaviour Cycle Logs) and top-down (theory driven) processes to formulate OPBs. A further strength of the present study was its prospective approach. This allowed for data to be collected systematically as it was entered on the clinical records, thus minimizing the chance of recall bias.
There are several issues that need to be considered when interpreting the results of this study. Firstly, this study evaluated OPBs in a small number of patients so although significant positive findings were evident further comparable research using a larger and more diverse participant population is required to confirm the reliability, validity and utility of Jones' practice algorithm. Although the number of participants in this study limits generalizability, the study aimed to pilot a new algorithm and to investigate whether it has sufficient validity to support further application on larger scale. This is important in ensuring optimal use of resources and clinicians' time.
In addition, there are several factors, which may have affected participation rate. For example, compared with routine clinical practice, where staff might have a good rapport with patients facilitating engagement in treatment, greater opportunity for observation and open discussion about patient's psychological issues, this study was conducted by an Honorary Researcher. Participants were unfamiliar with the researcher and participation was voluntary; available data was limited to that contained in patients' files. This is quite different to standard clinical practice where staff are generally trying to engage patients in treatment and management and therefore have increased access to a greater amount of clinical data to assess treatment needs and outcomes. In view of the above limitations and the positive findings of this study, it is possible that if this study is conducted by clinical staff in in-patient settings, the levels of OPBs and PABs could be higher. However, this needs further testing.
Although patients were involved in data collection and formulation, through the completion of the Behaviour Cycle Log, most of the data were derived from electronic records entered by clinical staff. This posed several issues: there was variation in the level of detail and amount of information entered by staff, which hampers comprehensiveness. This issue was addressed by identifying multiple sources of information for the specific behaviour and reviewing clinical notes 2 days before and after the behaviour. Also, entries into clinical files were not specifically related to the aims and purposes of the study; as such, some OPBs and PABs may have been observed but not reported in the clinical files.
A further limitation was posed by the use of multiple types of data triangulation, such as data and observer triangulation (collating data from multiple sources and observers) to increase validity and reliability (Flick, 1992). This procedure may have increased ‘leakage’ during the development of the reference formulations. This term refers to the use of multiple sources of information to generate hypotheses and formulations and to detect characteristics of follow-up behaviours, which in turn can lead to increased chance of detecting information that is being searched (C. Evans, personal communication). The researchers attempted to address this by following a structured protocol and testing the formulations against existing behaviours, which increased the chance of refuting them.
In addition to testing OPBs, this study set out to establish similarity between predicted and actual PABs. However, very few PABs were recorded so analysis of similarity was not possible. The low number of positive behaviours recorded raises concerns about bias when staff makes file entries. Although one reason for the lack of actual PABs may be due to the fact that the predicted PABs were invalid, or that prosocial behaviour did not emerge in these patients during the course of follow up, the present results may also suggest that when making clinical entries, staff are more likely to record problematic behaviour. This may be due to heightened focus on risk related behaviour. Finally, it may have been that patients who improved were granted increased unsupervised community leave; if these patients engaged in pro-social behaviour in the community these behaviours would have not been observed and recorded by staff. Future research on PABs could confirm the importance of establishing processes to help with the recognition and re-enforcement of PABs. It is also critical that staff extend their attention to PABs as the emergence of these behaviours could signal the development of pro-social change rendering the individual less likely to recidivate; this is critical information for risk assessors.
Lastly, although this study detected 15 aggressive behaviours, two participants were aggressive only once. This may provoke discussion on whether the OPB framework is a cost and time efficient tool for patients who do not display frequent aggressive behaviours on the wards. During the course of this study, several key areas emerged, which highlighted why exploring functional similarity across behaviours for all patients is crucial. Aggressive OPBs were the focus of this study because they are hypothesized to be more similar to sequences of behaviour evident in previous aggressive behaviours than other OPBs; if aggressive OPBs are topographically dissimilar to previous aggressive behaviours then it is conceivable that there would be very little similarity between aggressive behaviours in the community and in-patient behaviours. Since databases were reviewed to identify only aggressive OPBs, a number of topographically dissimilar OPBs may have occurred, which were not taken into considerations. More specifically, it is possible that non-aggressive OPBs exist, which have the same sequence and function as aggressive OPBs, but they differ in topography in that they are not represented by aggressive behaviour (e.g., disengaging from therapeutic activities, self-harm or defiant actions). In clinical practice non-aggressive OPBs may be more likely to go unnoticed and unchallenged than aggressive OPBs and thus may maintain offending behaviour. This may have significant impact on the assessment, intervention and management of offending behaviour in this population. An alternative explanation for the low number of aggressive behaviours observed could be that the patients have acquired Detection Evasion Skills (DES; Daffern, Jones, et al., 2007). Previous research (Daffern, Jones, et al., 2007) has noted that the absence of OPBs and PABs could signify that the individual has acquired skills to evade detection. Though all patients engaged in OPBs, which suggests DES were not fully establishes, the presence of DES must also be considered.
Conclusion and recommendations
The results of this study validate the OPB framework and suggest there is a similarity between predictions, based on OPB formulations derived from accumulated information on previous aggressive and other behaviour and actual in-patient aggressive behaviours. This finding has several important implications. Firstly, it suggests that in-patient OPBs can be predicted using a novel structured practice algorithm to establish a reference formulation and to generate OPB and PAB predictions. Prospective analysis of an individual's previous offending and personal history allows for important themes in an individual's offending to be identified, for the individual's full behavioural repertoire to be established and for its development and trajectory to be determined. This study has shown that predicted aggressive OPBs based on Jones (2010a) practice algorithm are similar to manifested aggressive behaviours within custody. This result differs from previous research on OPBs and emphasizes the need for clinicians to establish formulations based on an individual's entire history of aggressive behaviour, rather than their index offence alone, which has been shown to frequently lack similarity with in-custody aggressive behaviours (Daffern et al., 2009).
In relations to PABs, this study has highlighted an important clinical issue, namely how positive behaviours are recorded in clinical notes, and the extent to which they are considered with regard to patients' treatment and progress. It is possible that bias exists in the way clinical notes are recorded. Future research on PABs could re-affirm the need for establishing, in clinical settings, processes to help with the recognition and re-enforcement of PABs. It is also possible that by helping patients to recognize PABs this would positively impact on their belief in behavioural change and self-efficacy. However, this hypothesis needs testing.
The validity and reliability of the OPB practice algorithm explored here would be strengthened by replication of this study using a larger sample of in-patients or prisoners and including review and monitoring of a wider range of OPBs, in particular expanding the study of in-custody behaviours to include non-aggressive OPBs.