Judging Risk: Key Determinants in British Domestic Violence Cases

Journal of Interpersonal Violence27(8) 1489 –1518

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425792 JIV27810.1177/0886260511425792Robinson and HowarthJournal of Interpersonal Violence© The Author(s) 2012

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1Cardiff University, Cardiff, UK2University of Bristol, Bristol, UK

Corresponding Author:Amanda L. Robinson, School of Social Sciences, Cardiff University, Glamorgan Building, King Edward VII Avenue, Cardiff CF10 3WT, UK Email: [email protected]

Judging Risk: Key Determinants in British Domestic Violence Cases

Amanda L. Robinson1 and Emma Howarth2

Abstract

Data from the largest study to date of the working practices of British vic-tim support workers (known as Independent Domestic Violence Advisors or IDVAs) are used to provide insight into how “risk judgments” are made in cases of domestic violence. Using data from more than 2,000 victims, this study found a convergence between actuarial data and IDVAs’ risk judg-ments when the risk score was high, but in cases with a lower risk score, IDVAs often used their professional judgment to upgrade risk. Next, we iden-tified the specific factors underpinning IDVAs’ risk judgments. Consistent with existing research, we found that IDVAs relied on a subset of available information when forming risk judgments, and characteristics of the abusive situation, such as the escalation of violence, use of weapons, stalking, and significant injuries, were particularly salient to them. Furthermore, IDVAs paid attention to victims’ perceptions and when they felt very frightened or afraid of further injury then IDVAs were more likely to label them as high risk. Although we identified some encouraging overlap between the subset of factors informing risk judgments and those associated with victims’ reabuse at a later date, some notable differences indicate a need for messages from

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1490 Journal of Interpersonal Violence 27(8)

research about the significance of particular risk factors to be reinforced to frontline practitioners on a regular basis.

Keywords

domestic violence, risk assessment, structured judgment, actuarial, victim advocates

Introduction

Although there is a fairly well-developed body of evidence and assessment tools in some areas of violence prediction and prevention, relatively less is known about how practitioners determine who is at a “high risk” of harm from domestic violence and, in particular, the specific factors associated with these judgments. It is imperative that these judgments are subjected to empirical scrutiny, as they often govern the type and amount of resources that are directed toward individual cases, meaning that people are directly—and in some cases dramatically—affected by the accuracy and quality of these deci-sions. This article draws on data from the largest study to date of the working practices of British victim support workers, known as Independent Domestic Violence Advisors (IDVAs, see Howarth, Stimpson, Barran, & Robinson, 2009), to examine how risk assessment occurs on the frontline, with a view to supporting and enhancing practitioners’ decision-making processes around the risk that domestic violence poses and, ultimately, what to do about it.

IDVAs: Specialist Risk-Focused PractitionersOver the last decade, there has been concerted effort made within the United Kingdom to integrate the identification, assessment, and management of risk into the mainstream response to domestic violence (Robinson, 2010a). The introduction of the IDVA role in 2005 has made a major contribution to this effort, along with significant changes to policing practice and policy (National Policing Improvement Agency, 2008). The intervention offered by IDVAs builds on the advocacy model which has been shown to be effective in reduc-ing physical violence as well as some of the health-related sequela associated with domestic violence such as Effectiveness of a UK based advocacy inter-vention to tackle severe domestic abuse: Findings from a multi-site evaluation (Howarth & Robinson, 2011; Parmar, Sampson, & Diamond, 2005; Ramsay et al., 2009; Robinson, 2003, 2006; Sullivan, 1991; Sullivan & Bybee, 1999). IDVAs work with victims to provide a crisis response that aims to improve



Robinson and Howarth 1491

safety by increasing victims’ access to important resources, including practical help and social support (Robinson, 2009). They are considered specialist risk practitioners for three reasons: (a) they identify the level and type of risk that a victim faces, (b) they work primarily (although not exclusively) with victims considered to be at “high risk” of further abuse, and (c) they also aim to man-age risk by developing and implementing safety plans for victims and coordi-nating access with other agencies that may help to keep victims safe. Some IDVAs work in a dedicated IDVA service while others work in projects that also include wider services such as community outreach and refuge. Some of these projects are relatively newly established, whereas others have been in operation for more than 30 years. Despite these differences, all IDVAs must have completed an accredited training program which was designed specifi-cally for their role, and which focuses heavily on the identification of risk factors, and the assessment and management of risk.1 It is currently estimated that there are more than 1,000 IDVAs delivering specialist intervention across the United Kingdom (see www.caada.org.uk).

Risk Assessment and Management in the United KingdomIDVAs take a structured professional judgment approach to the assessment of risk. This approach attempts to bridge the gap between “unstructured clinical assessment,” where decisions are made without any constraints or guidelines, and “actuarial assessment,” where discretion is completely removed with the use of highly structured tools (Kropp, 2004). The approach aims to maximize the consistency associated with actuarial methods while also allowing professionals to be flexible in how they form their risk judg-ments. Operationally, practitioners not only consider a standard set of risk factors in every case but also use their own judgment of the case to reach conclusions about who is at higher or lower risk of violence. Integral to this approach is the use of a simple risk identification checklist which was devel-oped with the aim of assisting practitioners to make empirically guided deci-sions—in other words, to inform and structure their judgment about which women were most likely to be killed or seriously injured as a result of domestic abuse. The items included in the risk identification checklist were developed from retrospective reviews of domestic violence homicides and serious injuries occurring in the United Kingdom and elsewhere, a review of the empirical research literature as well as through consultation with victims and practitioners (Richards, 2003; Robinson, 2004). Items include those relating to the profile of the perpetrator, victim, and the characteristics of the abusive episode and there is a good deal of content overlap with other tools




used in the field such as the Spousal Assault Risk Assessment guide (SARA; Kropp, Hart, Webster, & Eaves, 1995) and the Danger Assessment (Campbell, 1986; Campbell, Webster, & Glass, 2009). Like earlier versions of the Danger Assessment (Campbell, 1986), the number of “yes” responses is meant to provide a basic indication of a victim’s level of risk, with the rec-ommendation that those scoring 10 or more should be considered “high risk” (see www.caada.org.uk). In other words, IDVAs translate the numerical risk score into a descriptive risk judgment (high risk or not). Consistent with the structured professional judgment approach, however, there is flexibility for IDVAs to make judgments that do not reflect the actuarial data (e.g., victims with low risk scores being classified as “high risk”).

As risk assessment is the first stage in the process of managing risk (Dutton & Kropp, 2000), decisions regarding the type and amount of assistance vic-tims are to receive follow from the risk judgments of IDVAs, with victims classified at “high risk” prioritized for different types and more intensive intervention (Howarth et al., 2009; Robinson, 2006). Thus, the risk judg-ments made by IDVAs have very real implications for victims of domestic abuse in Britain. Recent evaluations indicate the positive outcomes reported by victims receiving this type of intervention (see Robinson, 2010b for a review). For example, the multisite evaluation of IDVA services, from which the data for the current study are drawn, showed that the majority of victims who remained engaged with an IDVA service reported a cessation in abuse, indicating that together the information exchanged between an IDVA and a victim during a risk assessment and the advice and support that ensues com-prise an effective strategy for reducing risk in the immediate term (Howarth et al., 2009).

In the next section, we consider briefly evidence relating to the conver-gence between actuarial data and the risk judgments of practitioners, includ-ing studies that provide an account of discrepancies or gaps between actuarial information and professional decisions and those that explicitly look at the specific types of information included in actuarial risk tools that practitioners draw on when making risk judgments.

Previous Research on Judging RiskLittle debate exists over the value that actuarial information can add to pro-fessional decision making; however, even combining the most experienced and carefully formulated professional judgment and the best combination of empirically validated risk factors yields modest results at best (Bennett Cattaneo & Goodman, 2005; Coid et al., 2011; Goodman, Dutton, & Bennett,




2000; Heckert & Gondolf, 2004; Hilton & Harris, 2005; Hilton, Harris, & Rice, 2001; Webster, Harris, Rice, Cormier, & Quinsey, 1994; Weisz, Tolman, & Saunders, 2000). Reasons for this are twofold: (a) methodological limitations within empirical research on risk factors for reabuse and (b) human error translating actuarial information into risk judgments. As profes-sional discretion is preserved within the structured judgment approach, examining the convergence between actuarial data and risk judgments sheds some light on the contribution that one approach makes to the other. For example, Ansbro’s (2010) analysis of nearly 18,000 risk judgments by British probation officers revealed agreement with the actuarial score in 98% of cases. The very small percentage of cases representing a gap between the actuarial information and the risk judgment were informative for showing that probation officers were three times more likely to upgrade risk (when there was a low actuarial score) as they were to downgrade risk (from a high actuarial score). This is consistent with Kemshall’s (1998) notion of a “pre-cautionary principle,” where practitioners choose to err on the side of caution rather than underestimate risk. Ansbro suggests this gap may be inevitable, given that misjudgments that result in harm often result in serious and high-profile ramifications for the practitioner involved. The challenge of how to support practitioners to simultaneously use their professional judgment while neither under- nor overestimating risk has been highlighted in the first part of a wholesale review of the British child protection system, which warned that “compliance with regulation and rules often drives professional practice more than sound judgment drawn from the professional relationship and interaction with a child, young person and family” (Munro, 2010, p. 8). Furthermore, Ansbro found that decisions to upgrade risk were more com-mon among sexual offenders, whereas nonsexual offenders were more likely to benefit from decisions to downgrade their risk. Her study illustrates how convergence between actuarial data and risk judgments may be asymmetrical across different types of cases.

Gaps between actuarial information and professional judgment also can emerge from the style in which risk is communicated, as practitioners often use descriptive terms rather than numerical probabilities or summary scores. IDVAs, for example, prefer to discuss “high-risk” victims rather than victims with particular risk scores. Yet it was noted some time ago that the “transla-tion of nomothetic empirical data into idiographic conclusions” was inher-ently difficult, even among experts such as psychologists and psychiatrists (Heilbrun, O’Neill, Strohman, Bowman, & Philipson, 2000, p. 140). By way of illustration, Hilton, Carter, Harris, and Sharpe (2008) conducted a study into how numerical risk data were translated into nonnumerical terms such as




“low” or “high” risk. They found substantial disagreement among clinicians (even those working in the same institution) in their perceived upper and lower actuarial boundaries for these terms. Together, these findings indicate that, even when practitioners rely on actuarial data, disparities might creep into how they translate and communicate this information.

A further reason for actuarial/judgment disparities is that particular types of information are deemed more salient than others when appraising risk. Extant research reveals a filtering process whereby a subset of the total amount of information available is used to inform the judgments of police (Trujillo & Ross, 2008), victim advocates (Bennett Cattaneo, 2007), mental health professionals (Elbogen & Mercado, 2002), and even victims them-selves (Bennett Cattaneo, 2007; Connor-Smith, Henning, Moore, & Holdford, 2010). Furthermore, research suggests that greater emphasis is placed on a subset of information while other information that is perceived to be less relevant is disregarded—even when the utility of this information has been empirically validated. For example, work by Elbogen and Mercado (2002) revealed that in attempting to predict the risk of violence among patients detained in psychiatric inpatient facilities, practitioners perceived clinical information such as observation of behavior and compliance with medication regimes as more relevant than any of the items contained within a number of empirically validated risk assessment tools. Research in Australia showed that despite collecting extensive information using a structured risk assess-ment tool, in practice, police officers relied on a subset of historic and situation-specific information such as the progression of the incident, the number of previous incidents, and the involvement of drugs and alcohol in the current situation to make a decision about risk (Trujillo & Ross, 2008). Bennett Cattaneo (2007) found that victim advocates relied on some, but not all, of the information available to make a judgment about a woman’s risk of revictimization in the short term. Specifically, whether the victim had chil-dren in common with the perpetrator, the severity of physical and psychologi-cal abuse, and the perpetrator’s drug use were positively associated with advocates’ risk judgments. In victims’ assessments of risk, it has also been shown that key determinants include the levels of physical and psychological abuse, escalation of violence, and substance abuse by the perpetrator (Bennett Cattaneo, 2007; Connor-Smith et al., 2010). Clearly, both practitioners and victims selectively attend to a portion of available information to appraise risk, with situation-specific characteristics heavily influencing their decision making.

As the work reviewed above suggests, there are many points at which inaccuracy may be imbued into the risk assessment process. Practitioners




must rely on imperfect actuarial data when forming their decisions, they pre-fer to translate numerical scores into descriptive terms, identifying a particu-lar subset of items to be the most salient predictors of risk, all the while feeling an understandable pressure to err on the side of caution while also maintaining accuracy. We expect IDVAs to be similarly confronted by these challenges when forming risk judgments and it is therefore important to empirically investigate their decision making, not least because an accurate assessment represents the first step toward successfully managing risk (Douglas & Kropp, 2002; Dutton & Kropp, 2000). With the intention of informing efforts to support and enhance the accuracy of risk judgments made by specialist domestic violence practitioners working in the United Kingdom, this study examines three key questions: (a) to what extent does professional judgment converge with actuarial data, (b) what are the specific factors that IDVAs rely on to form their judgments of risk, and (c) which fac-tors are associated with revictimization at a later date. The implications of our findings are discussed with a view to improving risk assessment and manage-ment processes so that positive outcomes for victims are more likely.

MethodProcedure

At the point of intake (Time 1), IDVAs collected demographic information for each victim, information pertaining to the nature and severity of abuse, and the risk indicator checklist.2 Data were then gathered on a second occasion (Time 2), either at the closure of a case or after 4 months, as an interim marker of case progress for longer cases. Data gathered at Time 2 related to the interventions or types of support offered to victims during the course of their case, a second risk assessment and measures of any reabuse experienced since intake. Data were not gathered on a second occasion if a victim had disengaged from the service and/or the IDVA had no recent contact with her.

SampleData were collected from victims of domestic violence accessing seven IDVA services over a 27-month period (January 1, 2007-March 31, 2009). In total, data were collected from n = 2,930 female victims at Time 1. On aver-age, victims were 33 years old. Nearly one quarter (24%) were from Black/minority ethnic (BME) communities3 and 3% had insecure immigration status. Most victims (68%) had children. Relatively few victims (11%) were living with the perpetrator at the point of intake.




Data relating to physical revictimization at a later date (Time 2) was avail-able for n = 821 (28%) of these victims. Due to the significant attrition in the sample over time, analyses were performed to examine whether the Time 1 and Time 2 samples differed in any meaningful way. Results indicated that the samples were significantly different (p < .05) on 11 of the 27 risk factors on the checklist. Ten of the differences meant the risk factor was more pro-nounced in the Time 2 sample (e.g., criminal record, prior record for domes-tic violence, injuries causing significant concern, weapons causing significant concern, perpetrator’s alcohol misuse, jealous/controlling behavior causing significant concern, conflict over child contact, sexual abuse, stalking, isola-tion of the victim), whereas one difference was less likely in the Time 2 sam-ple (victim afraid of further injury). The Time 2 sample also had a significantly lower proportion of BME victims (19%) and a higher proportion of victims with children (77%).4 Thus, those victims “lost” to follow-up appear to be those that were experiencing less severe forms of abuse; those with more risk factors tended to remain engaged with the IDVA service and provided data at Time 2. All of these differences must be taken into account when interpreting the results and indicate that direct comparisons between the models should be made cautiously. Fundamentally, our Time 2 findings are more applicable to victims at higher risk.

MeasuresRisk assessment. As stated previously, IDVAs completed a risk checklist5 at

the outset of a case to help them gauge the level of risk posed to a victim. Each risk factor was measured using a yes/no dichotomous response format. The number of positively endorsed indicators was totaled into a summary risk score (possible scores ranging from 0-20). IDVAs used the checklist to come to a judgment about the victim’s risk of further harm (1 = high risk, 0 = not at high risk). In practice, a victim was deemed to be at high risk if 10 of the 20 risk factors on the checklist were endorsed and/or the IDVA perceived the level of risk to be high, irrespective of the risk score. This allowed IDVAs to exercise their professional judgment.

Physical revictimization. Physical reabuse was a key outcome measure for the study.6 Responses at Time 2 reflected the occurrence of any physical abuse experienced during the period of intervention or in the last 3 months if the period of intervention was lengthy (1 = experience of any physical abuse, 0 = no physical abuse recorded).

Intervention. Two measures were included in the Time 2 model to control for features of the intervention. IDVAs indicated which types of support and




advice had been received by the victim from a list of 18 options. The different types of support received by the victim was captured by an ordinal measure (1 = 0-1, 2 = 2-5, 3 = 6-10, 4 = 11 or more).7 A second measure reflected the frequency of support provided by the IDVA to the victim. IDVAs were asked to indicate the frequency of support on a 3-point ordinal scale (<5 contacts, ≥5 contacts, intensive contact). For the purpose of this study, we combined the latter two categories to create a dichotomous variable to give a basic indi-cation of frequency of contact (0 = <5 contacts, 1 = ≥5 contacts).

Sociodemographic characteristics of victims. Sociodemographic information was collected including the victim’s age, ethnicity (1 = BME, 0 = White), immigration status (1 = insecure, 0 = not), and children (1 = yes, 0 = no). Information was also gathered about the victim’s relationship with the perpe-trator and whether they lived together.

Analytic StrategyFirst, we analyzed the cases to identify the extent to which risk judgments con-verged with the actuarial information. The second stage of analysis involved identifying the factors associated with risk judgments made by IDVAs at case intake (Time 1). A logistic regression model specified the risk factors that were linked to risk judgments (1 = high risk, 0 = not at high risk). We also included the summary risk score in the model to determine whether this was associated with risk judgments, over and above the individual factors. Victim demographic factors were included in the model to identify whether judgments varied across different types of victims. Finally, the same model was used to predict physical reabuse at Time 2 with the addition of IDVAs’ risk judgments at Time 1 as well as two measures reflecting intervention received, to highlight particular risks or victim characteristics that practitioners should pay attention to when appraising risk and implementing risk management strategies. The tolerance statistics for the independent variables included in our models were all .60 or greater, indicat-ing very little chance of collinearity (Menard, 1995). In addition, variance infla-tion factors (VIFs) were assessed and did not indicate multicollinearity.

ResultsConvergence Between Actuarial Information and Risk Judgments

Out of a total of 2,930 cases, IDVAs judged the majority of victims as “high risk” at Time 1 (n = 2,236 or 76%) compared with “not at high risk” (n = 214




or 7%). In 480 cases (16%), the risk judgment was missing or the IDVA “didn’t know” (see Table 1). Just above half (52%) obtained a score of 10 or more on the risk indicator checklist (i.e., meeting or exceeding the recom-mended actuarial threshold for “high risk”), whereas 48% obtained a score of 9 or less (i.e., based on actuarial information alone, these victims pre-sented as lower risk). Analysis of the agreement of actuarial data (risk score) and professional judgment (high risk/not at high risk/don’t know) showed that IDVAs’ judgments concurred with actuarial data in half of all cases (49%), although, as Table 1 indicates, concordance with the actuarial data varied dramatically at higher and lower levels of risk as defined by the

Table 1. Actuarial Risk Scores by Risk Judgments

Risk judgment

Risk score Not high risk High risk Don’t know Total

0 0 10 1 111 0 9 1 102 0 18 0 183 2 32 1 354 8 54 3 655 13 66 7 866 39 141 59 2397 40 171 71 2828 34 208 86 3289 24 248 57 329

<10 160 (11%) 957 (68%) 286 (20%) 1,403

10 26 260 47 33311 9 258 42 30912 4 252 43 29913 9 184 27 22014 3 150 19 17215 2 92 11 10516 1 47 4 5217 0 25 0 2518 0 7 1 819 0 4 0 4

>10 54 (4%) 1,279 (84%) 194 (13%) 1,527

Total 214 2,236 480 2,930




actuarial scoring system. Results show that when actuarial data indicated a high level of risk (risk score ≥ 10), IDVAs rarely made a judgment to the contrary. Specifically, IDVAs judged 84% of these cases to be at high risk, 4% of cases were categorized as lower risk, and in 13% of cases, IDVAs were unable to assign a judgment and indicated “don’t know” or left the question blank.

By way of contrast, when the actuarial data indicated lower risk (risk score < 10), IDVAs often used their professional judgment to upgrade risk. In these cases, 68% were judged as high risk, 11% of cases were judged as lower risk, and in 20% of cases, IDVAs were unable to make a decision about risk. Therefore, at the lower end of the scale, IDVAs often were willing to override the actuarial data to make a judgment of “high risk.” It is also apparent that there was a higher rate of uncertainty as to how to interpret risk when fewer risk factors were endorsed and the case did not clearly meet the recommended threshold for high risk.

Factors Associated With Risk JudgmentsTable 2 displays the findings from the logistic regression analysis carried out to identify the type of information that was predictive of IDVAs’ risk judg-ments. The total number of risk factors endorsed on the actuarial tool (risk score) was not a significant predictor; however, the attributes of the abusive incident exerted a very powerful impact on risk judgments (escalating abuse: AOR8 2.90, 95% CI [1.98, 4.25]; injuries cause significant concern: AOR 3.09, 95% CI [1.81, 5.27]; use of weapons: AOR 2.12, 95% CI [1.10, 4.09]). Information about nonviolent forms of abuse also were associated with judg-ments as the presence of both stalking (AOR 2.67, 95% CI [1.61, 4.42]) and jealous/controlling behavior (AOR 2.41, 95% CI [1.71, 3.41]) more than doubled the odds of a high-risk judgment. The level of fear expressed by victims, particularly that of further injury, was also found to be important (AOR 2.07, 95% CI [1.35, 3.15]), suggesting that IDVAs paid attention to women’s assessment of their situation, although fears in relation to the well-being of children were not found to be associated with judgments. It is clear from the findings that IDVAs pay attention to both whether a victim is preg-nant (AOR 3.27, 95% CI [1.24, 8.63]) and whether she has children (AOR 1.57, 95% CI [1.12, 2.20]), with both elevating the odds of a high-risk judg-ment. The presence of children is not directly assessed by the checklist (although other child-related issues are), giving some indication that, in line with a structured judgment approach, IDVAs use more than just actuarial information when judging risk.




Table 2. Logistic Regression Model Predicting Risk Judgment at Time 1 (1 = high risk)

B SE Significance AORLower

CIUpper

CI

Criminal record 0.00 0.23 0.99 1.00 0.64 1.56Criminal record for DV 0.28 0.24 0.26 1.32 0.82 2.13Injuries—cause

significant concern1.13 0.27 0.00 3.09 1.81 5.27

Weapons—cause significant concern

0.75 0.33 0.02 2.12 1.10 4.09

Perpetrator’s financial problems

0.16 0.19 0.41 1.17 0.81 1.69

Perpetrator’s alcohol use −0.06 0.17 0.74 0.94 0.67 1.33Perpetrator’s mental

health−0.18 0.20 0.37 0.84 0.57 1.24

Perpetrator’s drug use −0.29 0.18 0.12 0.75 0.52 1.07Victim is pregnant 1.19 0.49 0.02 3.27 1.24 8.63Jealous/controlling

behavior0.88 0.18 0.00 2.41 1.71 3.41

Relationship separation −1.24 0.29 0.00 0.29 0.16 0.52Child contact conflict 0.35 0.21 0.10 1.42 0.94 2.15Threats to kill victim −0.03 0.19 0.88 0.97 0.67 1.41Threats to kill children 0.08 0.36 0.84 1.08 0.53 2.20Threats to kill other

partner0.51 0.40 0.21 1.67 0.75 3.68

Threats to kill other people

0.36 0.25 0.16 1.43 0.87 2.35

Strangle/choke −0.13 0.20 0.51 0.88 0.59 1.30Escalation of DV 1.07 0.19 0.00 2.90 1.98 4.25Suicide threats—

Victim0.20 0.22 0.35 1.22 0.80 1.86

Suicide threats—Perpetrator

−0.02 0.20 0.93 0.98 0.66 1.46

Sexual abuse −0.22 0.20 0.28 0.80 0.54 1.20Victim very frightened 0.46 0.22 0.04 1.59 1.03 2.46Victim afraid of further

injury0.73 0.22 0.00 2.07 1.35 3.15

Victim afraid perpetrator will kill children

0.27 0.22 0.23 1.31 0.84 2.03

(continued)





CIUpper

CI

Victim afraid perpetrator will harm children

0.31 0.28 0.26 1.37 0.79 2.36

Stalking 0.98 0.26 0.00 2.67 1.61 4.42Victim isolated −0.09 0.19 0.65 0.92 0.63 1.33Total risk score −0.08 0.08 0.35 0.93 0.79 1.09Victim’s age in years 0.02 0.01 0.02 1.02 1.00 1.04Victim is Black/minority

ethnic−0.39 0.19 0.04 0.67 0.47 0.98

Victim has insecure immigration status

−0.47 0.46 0.31 0.63 0.25 1.55

Victim has children 0.45 0.17 0.01 1.57 1.12 2.20Constant 0.84 0.44 0.06 2.31 −2 log likelihood 1,118.65 Nagelkerke R2 .256 % classified correctly 92.1

Note: AOR = adjusted odds ratio; CI = confidence interval; DV = domestic violence. N = 2,416

Two pieces of information decreased the odds of a high-risk judgment. First, IDVAs were less likely to judge a case as high risk when victims reported that they had recently or were in the process of separating from an abusive partner (AOR 0.29, 95% CI [0.16, 0.52]), although it is important to remember that an overwhelming majority of victims experiencing relation-ship separation were still classified as high risk (90%). Further investigation of this finding revealed that when “cohabit” (1 = victim and perpetrator liv-ing together, 0 = else) replaced “separation” in the multivariate model, the overall results (not presented here) were remarkably similar, although “cohabit” significantly increased the likelihood of a high-risk classification (AOR 1.89, 95% CI [1.02, 3.50]). This finding suggests that IDVAs were tak-ing account of victims’ proximity to the abuser as more a concrete indicator of risk and opportunity for violence, with those who had separated perceived as less proximal to the source of danger, rather than viewing the unique risk attached to relationship dissolution in and of itself. Second, BME victims were less likely to be considered high risk at Time 1 compared with their White counterparts (AOR 0.67, 95% CI [0.47, 0.98]). Further analyses showed that the rates of injuries and weapon use, threats to kill the victim,

Table 2. (continued)




Table 3. Logistic Regression Model Predicting Physical Abuse at Time 2 (1 = yes)


CIUpper

CI

Criminal record 1.14 0.32 0.00 3.12 1.66 5.87Criminal record for

DV0.10 0.30 0.74 1.10 0.61 1.99

Injuries—cause significant concern

1.29 0.27 0.00 3.64 2.14 6.19

Weapons—cause significant concern

1.21 0.31 0.00 3.35 1.83 6.10

Perpetrator’s financial problems

0.44 0.26 0.09 1.55 0.93 2.59

Perpetrator’s alcohol use

0.25 0.24 0.30 1.29 0.80 2.07

Perpetrator’s mental health

−0.12 0.27 0.66 0.89 0.52 1.50

Perpetrator’s drug use

−0.18 0.25 0.49 0.84 0.51 1.38

Victim is pregnant 0.30 0.50 0.54 1.36 0.51 3.60Jealous/controlling

behavior0.58 0.25 0.02 1.79 1.10 2.91

Relationship separation

0.88 0.36 0.02 2.42 1.19 4.95

Child contact conflict

0.80 0.29 0.01 2.23 1.27 3.91

Threats to kill victim

0.17 0.28 0.55 1.18 0.68 2.06

Threats to kill children

−0.16 0.38 0.68 0.85 0.40 1.81

Threats to kill other partner

−0.45 0.43 0.29 0.64 0.27 1.48

Threats to kill other people

−0.07 0.29 0.80 0.93 0.53 1.63

Strangle/choke 0.69 0.27 0.01 1.99 1.16 3.40Escalation of DV 0.30 0.33 0.36 1.35 0.71 2.55Suicide threats—

Victim0.50 0.28 0.08 1.64 0.95 2.84

Suicide threats—Perpetrator

0.44 0.26 0.09 1.56 0.93 2.61

Sexual abuse 0.58 0.27 0.03 1.79 1.05 3.05

(continued)





CIUpper

CI

Victim very frightened

0.36 0.37 0.33 1.43 0.70 2.93

Victim afraid of further injury

−0.16 0.36 0.66 0.85 0.42 1.74

Victim afraid perpetrator will kill children

0.68 0.29 0.02 1.97 1.11 3.49

Victim afraid perpetrator will harm children

0.68 0.30 0.03 1.97 1.09 3.56

Stalking 1.49 0.28 0.00 4.45 2.55 7.78Victim isolated 1.26 0.26 0.00 3.52 2.11 5.88Total risk score

(Time 1)−0.57 0.11 0.00 0.57 0.46 0.71

Professional judgment (Time 1)

−0.02 0.47 0.96 0.98 0.39 2.47

Victim’s age in years −0.01 0.01 0.23 0.99 0.96 1.01Victim is Black/

minority ethnic−0.99 0.30 0.00 0.37 0.21 0.67

Victim has insecure immigration status

1.02 0.57 0.07 2.78 0.92 8.47

Victim has children 0.20 0.28 0.48 1.22 0.70 2.14Types of support

(0-1)0.30

Types of support (2-5)

0.17 0.46 0.72 1.18 0.48 2.90

Types of support (6-10)

0.48 0.51 0.35 1.61 0.60 4.37

Types of support (11+)

−1.49 1.35 0.27 0.23 0.02 3.16

Frequency of support (≥5 contacts)

−1.23 0.25 0.00 0.29 0.18 0.48

Constant −0.17 0.83 0.84 0.85 −2 log likelihood 537.24 Nagelkerke R2 .351 % classified

correctly84.3

Note: AOR = adjusted odds ratio; CI = confidence interval; DV = domestic violence. N = 681.

Table 3. (continued)




stalking, pregnancy, child contact issues, separation, stalking, and the overall number of factors endorsed did not differ across groups. Although BME vic-tims tended to report less serious perpetrator profiles (i.e., fewer perpetrators with criminal records, financial problems, mental health issues, drug/alcohol use, suicidality, fewer threats to kill others, and lower rates of jealous/con-trolling behavior), they themselves were more likely to be very frightened, afraid of further injury, and afraid the perpetrator would harm or kill the chil-dren. BME victims were more likely to report sexual abuse and escalation of abuse. This mixed bag of findings does not reveal a clear pattern of results whereby factors tending to increase the odds of a high-risk judgment were systematically less likely for BME victims.

The model correctly predicted risk classification in 92% of cases, although the goodness of fit estimates indicate that the model only accounts for around a quarter of the variance in the outcome measure, suggesting that information in addition to what is considered here informs IDVAs’ risk judgments.

Risk Factors Linked to Physical RevictimizationIn the last analytic step, we reran the model discussed above with the addi-tion of IDVAs’ risk judgments and two intervention variables and using physical reabuse as the outcome variable, to ascertain whether there was any continuity or indeed discontinuity between the factors underpinning IDVAs’ judgments of risk and those predicting revictimization (see Table 3). In terms of overlap between the two models, results revealed that serious injuries (AOR 3.64, 95% CI [2.14, 6.19]), the use of weapons (AOR 3.35, 95% CI [1.83, 6.10]), and the presence of jealous/controlling behavior (AOR 1.79, 95% CI [1.10, 2.91]) and stalking (AOR 4.45, 95% CI [2.55, 7.78]) at Time 1 were related to the increased odds of reabuse, and they also predicted IDVAs’ risk judgments. In addition, conflict over child contact, which approached significance in the previous model, was related to physical revic-timization (AOR 2.23, 95% CI [1.27, 3.91]).

Although there were differences between the two samples as we noted earlier, making direct comparisons between the models unreliable, an analy-sis of discontinuity between them is informative, given that this highlights important predictors of revictimization that tend to be overlooked by practi-tioners, information which in turn may be important for shaping training efforts. Factors that were linked to physical reabuse yet did not inform prac-titioners’ judgments included perpetrators’ criminal records (AOR 3.12, 95% CI [1.66, 5.87]), attempts to strangle/choke the victim (AOR 1.99, 95% CI [1.16, 3.40]), sexual abuse (AOR 1.79, 95% CI 1.05, 3.05]), victims’ fears for




their children’s safety (that the perpetrator would kill them: AOR 1.97, 95% CI [1.11, 3.49]; or harm them: AOR 1.97, 95% CI [1.09, 3.56]), and victim isolation (AOR 3.52, 95% CI [2.11, 5.88]). In addition, relationship separa-tion increased the odds of physical revictimization (AOR 2.42, 95% CI [1.19, 4.95]), which, while expected based on a raft of other studies, is of particular note, given that in the Time 1 model, separation or the intention to do so seemed to signal to IDVAs that victims faced less risk than other victims.

Interestingly, the total number of risk factors on the checklist was nega-tively related to physical revictimization (AOR 0.57, 95% CI [0.46, 0.71]), indicating perhaps counterintuitively that those with lower risk scores were more likely to experience physical abuse at Time 2 (AOR 0.57, 95% CI [0.46, 0.71]). Higher risk as indicated by more risk factors may increase help- seeking efforts, meaning that victims experiencing a greater number of risks were more likely to remain engaged with services and receptive to support from IDVAs. Indeed, the mean risk score for the Time 2 sample was signifi-cantly higher than that for the Time 1 sample, lending some support for this interpretation (F = 8.53, df = 1, p < .01). Alternatively, this finding may be indicative of the prioritizing of resources according to risk, with those with higher risk scores receiving more frequent and intensive support, which may in turn have a bearing on victim outcomes. Consistent with this was the find-ing that more frequent support was associated with lower odds of subsequent physical abuse (AOR 0.29, 95% CI [0.18, 0.48]), which in turn may be indic-ative of a dose response relationship between intervention and outcome. Only one sociodemographic characteristic was significantly related to physical reabuse. BME victims had a significantly lower likelihood of experiencing physical abuse at Time 2 (AOR 0.37, 95% CI [0.21, 0.67]). This finding is consistent with that from the Time 1 model which showed that BME victims were less likely to be labeled as high risk.

The model correctly predicted physical reabuse in 84% of cases and explained about one third of the variance in the outcome measure, suggesting that other predictors (not included in our model) are important for explaining repeat violence.

DiscussionAlthough there is a fairly well-developed body of literature on violence pre-diction and prevention, relatively less is known about how practitioners determine who is at “high risk” from domestic violence and, in particular, the specific types of information that they draw on when formulating risk judg-ments. The current work is the first study undertaken to offer insight into




the process by which risk assessment is carried out by specialist domestic violence practitioners operating in the United Kingdom. We consider find-ings relating to the three key aims of our research in detail below.

First, our findings show that although the majority of cases were judged to be at high risk, convergence depended on whether the actuarial data indicated higher or lower risk. As Ansbro (2010) found, we observed that practitioners often overrode the actuarial information when it indicated lower risk, whereas convergence was more likely when actuarial information indicated high risk. Rather than viewing this finding as evidence of errors in judgment, we specu-late that IDVAs may tend toward interpreting few positive responses on the risk checklist as an indication of a victim’s failure to disclose or accept the reality of her situation rather than a true absence of risk. This tendency to err on the side of caution is not unique to the domestic violence field but is evi-dent more broadly among practitioners whose job is to prevent individuals under their supervision inflicting or suffering harm. This is an inevitable way of working in a climate where, following a serious event such as a child death or domestic violence homicide, it is common to look for failure in practice and to hold individual practitioners publically accountable (Kemshall, 1998; Munro, 2010). Understanding the context within which risk is judged has been noted as crucial (Ansbro, 2010; Hilton et al., 2008; Kemshall, 1998, Munro, 2010) and extant research on IDVAs, although limited, reveals the unremitting pressure they are under to process a high volume of cases both efficiently and accurately (Howarth et al., 2009; Robinson, 2009). Evidence that they prefer to err on the side of caution rather than accept low actuarial scores at face value, therefore, is not surprising. Indeed, it directly affected 957 victims who, with actuarial scores less than 10, otherwise would not have received their specialist intervention.

Second, the logistic regression analysis showed that, whilst the total num-ber of risk factors was not linked to IDVAs’ risk judgments, specific types of information did affect on their decision making. Consistent with other research, we found evidence of a filtering process as only a subset of factors significantly affected risk judgments. Furthermore, situation-specific infor-mation relating to the most recent incident of abuse featured prominently in these decisions (Bennett Cattaneo, 2007; Trujillo & Ross, 2008). The finding that an incident representing a progression of violence, that involved weap-ons and which resulted in significant injuries, increased the likelihood of assigning a high-risk label is intuitive and indeed indicates practitioners’ adherence to commonsense norms about “risky” situations.

Nonphysical forms of violence such as jealous/controlling behavior and stalking were also important to risk formulations which is encouraging, given




that research highlights these as empirical correlates of revictimization (Bennett Cattaneo & Goodman, 2003, McFarlane et al., 1999; Walton-Moss, Manganello, Frye, & Campbell, 2005) and there has been considerable effort over the last few years among researchers and policy makers alike to concep-tualize domestic abuse as multifaceted, including physical, psychological, financial, and sexual components. Interestingly, the presence of sexual abuse was not related to risk judgments, despite practice-based and empirical stud-ies indicating that domestic sexual abuse is a key correlate for severe and lethal violence (Campbell et al., 2003; Dobash, Dobash, Medina-Ariza, & Cavanagh, 2007; McFarlane & Malecha, 2005; Richards & Baker, 2003). However, it is important to remember that information relating to risk was gathered at a single time point, at the very outset of case, at which point sexual abuse may have been underreported, either because women were not comfortable to disclose intimate and distressing details to an unfamiliar worker or because, at the early stages of help seeking, they simply did not recognize sexual coercion or forced sex as part of the abuse they were expe-riencing (Bergen, 1996). Although it is reasonable to suppose that practitio-ners would be mindful of these issues, our model indicates that this risk factor did not influence their judgments. More research into this issue would be helpful.

Consistent with Trujillo and Ross’s (2008) study of police risk assess-ments, our findings showed that IDVAs paid careful attention to victims’ appraisals of their situation, with expressions of general levels of fear and the specific fear of sustaining further injury found to increase the odds of a high-risk classification. Higher levels of fear may be perceived as borne out of more severe abuse, with some evidence to support this contention (Thompson, Saltzman, & Johnson, 2001). Although these findings indicate that IDVAs were operating in line with practice-based recommendations and empirical research (Bowen, 2011; Campbell, 2004; Robinson, 2007; Weisz et al., 2000), they would be well advised to remember that victims’ perceptions are just one piece of information among many that should be considered when judging risk.

Interestingly, we found that the only factor to decrease the odds of a high-risk judgment was the presence of a recent or impending separation, which is surprising given the volume of research and practice-based literature that cites separation occurring up to a year previously as a potent predictor of reabuse and homicide (Moracco, Runyan, & Butts, 1998; Wilson & Daly, 1993). Further exploration of this finding revealed that living with the abuser increased the odds of a high-risk judgment, suggesting that practitioners may focus on victims’ physical proximity to the perpetrator, with cohabitation and




separation perceived as opposite ends of the same scale. Separation might also symbolize to IDVAs a victim’s commitment toward achieving safety for herself, in turn leading IDVAs to view with more hope the effect of any safety measures they put in place. To improve the accuracy of risk judgments, it is crucial that training helps practitioners to recognize living arrangements and relationship status as independent, although sometimes overlapping, pieces of information—both of which are important to appraising risk. Furthermore, practitioners need to appreciate the relatively long window of time over which a relationship separation can still be a relevant risk to consider.

It was notable that perpetrators’ criminogenic characteristics (e.g., crimi-nal record, substance misuse) were not linked to risk judgments. Both Bennett Cattaneo (2007) and Trujillo and Ross (2008) found perpetrators’ substance misuse to inform frontline practitioners’ judgments of risk in domestic vio-lence cases, although, like us, neither study found indicators of prior criminal behavior (prior arrest, issue of an intervention order) to be related to risk judgments. There is much evidence that these factors correlate with an increased risk of reabuse and harm and are important to consider in assessing risk (see Bennett Cattaneo & Goodman, 2005 for a review). Indeed, the initial study from which these data are sampled found that the presence of any one of these factors significantly decreased the odds of victims achieving positive outcomes (Howarth et al., 2009). Despite the adage “past behaviour is the best predictor of future behaviour,” our research shows this does not inform frontline practitioners’ judgments of risk. A plausible explanation is that information relating to the perpetrator is simply less salient to IDVAs because they have limited remit to invoke strategies that ameliorate these risks, so less consideration is given to their presence. Heilbrun et al. (2000) observed that risk factors deemed to have a dual function in predicting risk and feeding into risk management plans were perceived to be more relevant to practitioners. With this point in mind, the static nature of some of these factors may further reduce their perceived relevance as they are seen as “unchangeable” and redundant to safety planning. This explanation could also apply to our finding that prior threats made by perpetrators were not associated with IDVAs’ risk judgments.

The tendency for practitioners to pin risk judgments on dynamic informa-tion without due consideration of extremely important static information has been demonstrated by others (Ansbro, 2010; Elbogen & Mercado, 2002), and whilst scholars contend that those concerned with violence prevention should pay special attention to variables that can be changed to make a difference to the course of events (Dutton & Kropp, 2000), they do not argue for practitio-ner to ignore static factors—rather they recommend that both types of




information are considered when judging risks (Dutton & Kropp, 2000; Snow Jones & Gondolf, 2001). These findings underscore the need for training to clearly illustrate the importance of perpetrator information, including their broader psychological functioning and previous criminal or antisocial behavior—whether directly related to domestic abuse or not.

In addition to the information gathered using the checklist, our study dem-onstrated that IDVAs draw on wider information when appraising risk. For example, the presence of children was found to be influential and most likely reflects the notion that children increase the likelihood of continued contact and opportunity for abuse (Bennett Cattaneo, 2007; Beeble, Bybee, & Sullivan, 2007; Humphreys & Thiara, 2002). The presence of children prob-ably provided a clear indication to IDVAs about the types of risk management strategies that should be mobilized, and as noted previously, information with this function is perceived as highly useful by practitioners, perhaps irrespec-tive of whether there is an empirical association with revictimization (Heilbrun et al., 2000).

The finding that BME victims were less likely to be judged as high risk is harder to explain. Some evidence suggests asymmetry in the types of abuse experienced by White and non-White groups (Renner & Whitney, 2009) and whilst we found lower rates of jealous/controlling behavior (which increased high-risk judgments) and slightly higher rates of sexual abuse (which was not linked to risk judgments), there were no observed differences in weapon use, injuries, stalking, or pregnancy which might explain this finding. Although beyond the scope of this article, it may be that there were systematic differ-ences across services in the risk assessment process, with those urban ser-vices working with a greater proportion of BME clients tending to make more conservative judgments about their cases. Another explanation is that other unmeasured variables correlated with ethnicity decreased the odds of a high-risk judgment.

Related to this last point, it is important to keep in mind that we were only able to include a token amount of the additional information which IDVAs might consider when judging a case and it is extremely likely, especially in light of the moderate amount of variance in the outcome variable accounted for by this model, that other information is important in helping IDVAs judge risk. Gaining some visibility of what this information is and the valence of its influence will be essential to develop a more in-depth understanding of, and bringing a greater degree of transparency to, the risk assessment process. Further exploration of practitioners’ ideas about the function of different types of information in their risk assessment and management decisions is also warranted.




In comparing the factors associated with revictimization with those that practitioners used in the formulation of their judgments, we found a number of commonalities between the two models, which is notable given that we took account of the different levels of intervention received by victims in the second model. Our results indicated that the infliction of injuries, use of weapons, and the presence of nonphysical forms of abuse (jealous/control-ling behavior and stalking) were linked both with IDVAs’ initial judgments of risk as well as victims’ reabuse. This is consistent with a considerable amount of research that pinpoints the intention to cause harm, use of weapons, and controlling behavior as factors that discriminate between lethal levels of vio-lence and that which is less severe (Campbell et al., 2003; Echeburúa, Fernández-Montalvo, Corral, & López-Goňi, 2009). These findings suggest that IDVAs’ concerns about these risks at the outset of a case are well placed and should be supported, although there may be a need to review risk man-agement strategies, given that they continued to exert an effect over time. Conflict over child contact was also an area of overlap indicating that IDVAs correctly assessed child contact arrangements as presenting opportunities for further violence. Although attrition in the sample over time necessitates cau-tion before drawing firm conclusions, these results can be considered a very positive reflection on the risk-based work of IDVAs, who are relatively new workers in the British approach to domestic violence. As the Time 2 sample represented victims who were experiencing relatively more severe abuse and who were perhaps more motivated in their help seeking, it is possible that a different pattern of results and perhaps less commonality may have been observed if it had been possible to run both models using the larger Time 1 sample.

The only demographic factor found to be common across both models was ethnicity. The finding that ethnicity was associated with lower odds of revic-timization appears to indicate that IDVAs were correct in their assessment of them as less at risk, as indeed less physical abuse was reported by these vic-tims at Time 2. We hesitate to draw firm conclusions about this finding, how-ever, as a similar effect is not replicated consistently in other research (see Bennett Cattaneo & Goodman, 2005 for a review). Similarly, there is little evidence pointing toward ethnicity as a moderator of treatment effects, with minority women faring better than their counterparts. One possibility may lie in the finding that BME victims reported being more fearful at Time 1, and an increased level of fear has been shown to be related to increased help-seeking efforts through both formal and informal channels (Barrett & St. Pierre, 2011). With no cut and dry explanation for these findings, further research is necessary to understand the impact of ethnicity, both on the risk of reabuse and on practitioners’ risk judgments.




As well as continuity between the two models, our results showed that a number of risk factors overlooked by practitioners in formulating their initial assessments of risk were correlated with physical reabuse, even after taking into account the resources accessed and the frequency of support that victims received. Failure to attend to this information may have a bearing not only on initial risk assessment but also on the management of risk that ensues. If a practitioner does not understand the role that a particular variable plays in increasing the risk of revictimization, they are perhaps less likely to mobilize strategies to address the particular risk. Specifically, we found that perpetra-tors’ general criminal history, victims’ fear that the perpetrator would harm or kill the children, prior nonlethal strangulation or choking, sexual abuse, rela-tionship separation, and victim’s isolation were all associated with increased odds of revictimization. We acknowledge that the attributes of the signifi-cantly smaller, higher risk sample at Time 2 may in part account for these differences and therefore caution should be exercised before making direct comparisons between the models, but nevertheless, these results highlight potential areas for training. In particular, there is an urgent need to address IDVAs’ misconceptions about the role of separation in predicting risk, given that they considered women separated from their partners as less at risk, when in fact the opposite is true.

Taken together, our findings show that IDVAs were not only guided by actuarial data but also used their discretion when judging risk, consistent with a structured judgment approach. We observed significant asymmetry in the convergence between actuarial data and risk judgments, with IDVAs tending to err on the side of caution and upgrade risk, probably, because they consid-ered the actuarial information at their disposal to be incomplete rather than to truly reflect lower risk. We found that a subset of factors was used to judge risk, with situation-specific information being particularly influential in the formulation of judgments, and information relating to perpetrators’ crimino-genic profiles, in particular, less so. Finally, although we identified some encouraging overlap between the subset of factors informing risk judgments and those associated with reabuse, some notable differences indicate a need for messages from research about the salience of particular risk factors to be reinforced on a regular basis.

Despite enormous strides forward in the field, we must not forget that risk assessment in cases of domestic violence is not an exact science and practi-tioners engaging in this work will need support. Even among experienced IDVAs, risk assessment is perceived as a highly skilled practice, in need of constant maintenance, especially because it relies at least in part on their professional judgment. Raising awareness of the “precautionary principle”




and supporting practitioners to make accurate judgments without artificially upgrading risk are key challenges for the future practice of IDVAs. Research comparing the correlates of high-risk judgments in IDVA versus other types of workplaces, and ethnographic studies of workplaces where risk-based work is routine, would be very informative in this regard.

Acknowledgment

The authors would like to thank the anonymous reviewers for their constructive and insightful comments on an earlier version of this article. The authors would like to thank Coordinated Action Against Domestic Abuse (CAADA) for making the data available for this study.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Hestia Trust, The Sigrid Rausing Trust, and the Henry Smith Charity.

Notes

1. CAADA (Coordinated Action Against Domestic Abuse) is a national charity that provides an accredited training program on behalf of the Home Office for Inde-pendent Domestic Violence Advisors and Independent Sexual Violence Advisors (see www.caada.org.uk).

2. Independent Domestic Violence Advisors (IDVAs) collected data from victims who explicitly engaged with their services. Each participating service had a con-sent procedure that victims were taken through as standard as part of the initial contact with an IDVA. IDVAs were able to indicate nonconsent using an “opt out” check box which prevented a victim’s data being included in the data set available for analysis.

3. The Black/minority ethnic (BME) group included Asian (Indian, Pakistani, Ban-gladeshi, South East Asian, Chinese, Japanese, or Other), Black (Caribbean, African, Somali, Other), Mixed (White and Black Caribbean, White and Black African, White and Asian, any other mixed background), and Other (Arab/Middle Eastern, Latin American, Traveller/Gypsy/Romanian). BME respondents were evenly spread across these four main ethnic groups; therefore, for ease of analysis and interpretation, they were combined into a dichotomous variable.

4. These results differ from those in the original report as we assessed attrition using a specific measure (physical reabuse at Time 2) rather than a generic measure




(any reabuse at Time 2). Regardless of the measure used, however, the message remains the same in that the Time 2 sample can be considered at higher risk.

5. There has been wide support for the checklist’s face and content validity, although to date there have not been any empirical studies undertaken to evaluate its con-vergent or predictive validity. The risk identification checklist is widely used by a range of frontline practitioners and forms the basis of DASH 2009 which is the risk tool recommended by the Association of Chief Police Officers (ACPO) and CAADA.

6. IDVAs were required to complete a “severity of abuse grid” at both Time 1 and Time 2. This measure was developed for the purposes of this project to provide IDVAs with a simple tool with which to gather standardized data relating to the type, severity, and frequency of four types of abuse (physical abuse, sexual abuse, perpetrators’ jealous and controlling behavior, and harassment and stalking). The design of this tool was based on the severity of violence matrix developed by Mederos, Gamache, and Pence (2006). IDVAs were required to indicate the pres-ence of each of these four types of abuse and, on endorsement of any type of abuse, severity was indicated on an ordinal scale (moderate, high, severe). Each level of severity was illustrated by detailed examples of behavior, specific to each form of abuse. For the purposes of this article, we focused exclusively on physical abuse at any level of severity.

7. Our decision to recode the continuous intervention variable into a smaller cate-gorical format was based on the proportional spread of responses. We feel the cat-egorical measure facilitates a more easily interpretable practice-based message. Using the original continuous measure in our model produced identical results; therefore, we have retained the recoded version of the variable.

8. AOR = adjusted odds ratio. Adjusted odds ratios are reported, indicating the asso-ciation between the independent variable and the odds of the outcome variable, after controlling for the simultaneous effects of other variables in the model.

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Bios

Amanda L. Robinson received her PhD in interdisciplinary social science from Michigan State University, with concentrations in criminology, sociology, and indus-trial/organizational psychology and is now a senior lecturer in criminology at Cardiff University, Cardiff, UK. She has conducted empirical research into American and British criminal justice systems on topics such as police investigation, community policing, violence against women, and specialist courts. She is currently involved in a Daphne III project funded by the European Commission on protecting high-risk victims of violence. She has published in Criminology & Criminal Justice, Criminal




Justice & Behavior, Violence Against Women, British Journal of Criminology, Policing & Society, and the Howard Journal of Criminal Justice.

Emma Howarth received her doctorate from Cardiff University in 2008, having undertaken research on children’s development in family contexts marked by hostile and violent interparental conflict. Since completing her PhD, she has led a large-scale evaluation of specialist domestic abuse services and has been involved in implement-ing a national evaluation framework for specialist services. She is currently a research associate and is part of a multidisciplinary team, undertaking a randomized controlled trial examining the effectiveness of a psychological intervention for women accessing specialist domestic abuse services.



Judging Risk: Key Determinants in British Domestic Violence Cases

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