Media Analysis

Original Research

Estimating the Relationship Between Perceived Stigma and Victimization of People With Mental Illness

Michelle N. Harris, MS1,2 , Miranda L. Baumann, MS1, Brent Teasdale, PhD3, and Bruce G. Link, PhD4

Abstract

Over the past two decades, we have substantially increased our under-

standing of violence committed by individuals with mental illness, while

comparatively less is known about the victimization experiences of this

population. What has been established in the literature is that individuals

with mental illness are more likely to experience victimization than the

general public, and certain risk factors influence the likelihood of victimiza-

tion. What remains unexplored is the possibility that a person with mental

illness’ perception that mental illness is stigmatized may be significantly

associated with victimization experiences. Thus, the purpose of the current

study is to examine whether stigma and victimization are associated, and in

what direction. In other words, does perceived stigma lead to victimization?

Or does victimization lead to perceived stigma? To assess these research

questions, data from the Community Outcomes of Assisted Outpatient

1Georgia State University, Atlanta, USA 2The University of Texas at Dallas, Richardson, USA 3Illinois State University, Normal, USA 4University of California, Riverside, USA

Corresponding Author:

Michelle N. Harris, Department of Criminology and Criminal Justice, Georgia State University,

33 Gilmer St SE, Atlanta, GA 30302, USA.

Email: [email protected]; [email protected]

Journal of Interpersonal Violence

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DOI: 10.1177/0886260520926326

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2022, Vol. 37(3-4) NP1226 –NP1252

Harris et al. NP1227

Treatment study are used, which is a longitudinal study of individuals with

serious mental illness (n¼ 184). A variety of methods are employed to

assess the association between victimization and perceived stigma including

logistic and ordinary least squares regression models. Results from the

logistic regression model indicate that perceived stigma is associated with

an increase in the odds that a person with mental illness will experience

victimization at later follow-ups. Results from the ordinary least squares

regression analysis, however, show that victimization at baseline does not

predict perceived stigma at later times. Implications regarding future

research and clinical practice are discussed.

Keywords

perceived stigma, mental health, victimization, violence exposure, longitu-

dinal data

Introduction

Considerable research attention has focused on violence committed by

individuals with mental illness (see Elbogen & Johnson, 2009; Estroff

et al., 1994; Hiday, 1997; Mulvey, 1994; Silver, 2006; Swartz et al.,

1998), whereas victimization experiences among this population have

received much less attention (see Khalifeh et al., 2016; Monahan et al.,

2017). In response to this imbalance, a handful of investigators have

called for increased attention to victimization, and have begun to

explore this important domain (e.g., Hiday et al., 1999, 2002; Silver,

2002, 2005; Teasdale, 2009; Teasdale et al., 2014). What is known at this

point is that individuals with mental illness are at increased risk of

victimization experiences when compared with the general population

(Goodman et al., 2001; Hiday et al., 1999, 2002; Silver, 2002; Teplin

et al., 2005; Walsh et al., 2003). In fact, some studies have suggested

that people with mental disorders experience from two (Hiday et al.,

1999; Walsh et al., 2003) to four (Teplin et al., 2005) times the risk of

violent victimization when compared with the general population. In addition, people with mental disorders are subject to stigma and its

consequences. Common conceptualizations of stigma include an,

“attribute that is deeply discrediting” (Goffman, 1963, p. 3), a character-

istic of persons that is contrary to societal norms (Stafford & Scott, 1986),

or a characteristic that is devalued in the social context (Crocker, 1999).

Unfortunately, people with mental illness, who sometimes display bizarre

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behaviors or fail to fulfill traditional societal expectations, have been exten- sively stereotyped and stigmatized (Link et al., 1999). In fact, people with mental illness are often portrayed in media outlets as ineffective in fulfilling societal roles, as well as being threats to community safety (Gerbner et al., 1981; Myrick & Pavelko, 2017; Owen, 2012).

Given that people with mental illness are at increased risk for vic- timization, and are subjected to stigma, a question that arises is whether there is a relationship between the two phenomena. Is it possible that stigma is significantly associated with the victimization experiences of people with mental illness? As Link and Phelan (2001) argue, cultural stereotypes play an important role in understanding stigma. The cul- tural stereotype of mental illness may lead the general public to believe that individuals with mental disorders are dangerous (Link et al., 1999). As a consequence of this perception, members of the public may desire increased social distance from individuals with mental illness (Link et al., 1999). This strong stereotype, or stigma, of people with mental illness as dangerous and violent (Pescosolido et al., 1999) may contrib- ute to their victimization experiences. That is to say, as theorized by other scholars, it is possible that people with mental disorders may be victimized because of the stigma associated with having a mental illness (Teasdale, 2009). If this stereotype triggers fear and anxiety, individuals in the general public may preemptively defend themselves against people they perceive as dangerous, thus victimizing them, purportedly in self-defense. Alternatively, this stigma may drive away potential guardians, leaving stigmatized individuals vulnerable to victimization.

To assess empirically the role of stigma in the experience of victim- ization among people with mental disorders, data from the Community Outcomes of Assisted Outpatient Treatment (AOT), a longitudinal study of individuals with serious mental illness (Link et al., 2011), will be utilized. The primary goal of our study is to determine whether stigma is significantly associated with victimization experiences among people with mental illness.

Literature Review

Violent Victimization of Individuals With Mental Disorders

Despite the stereotypes of dangerousness associated with mental disor- der, individuals with mental illness are actually more likely to be victims of violence than its perpetrators (Choe et al., 2008; Latalova et al., 2014; Maniglio, 2009). Although prevalence rates for violent victimization

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vary by type of victimization assessed, study population, and study design (Choe et al., 2008; Latalova et al., 2014), researchers have con- sistently reported prevalence ratios several times higher than the general population (e.g., Goodman et al., 1999; Maniglio, 2009). For example, one study comparing violent victimization rates from a subset of the National Crime Victimization Survey (NCVS) with a sample of people receiving treatment for serious mental illness found that roughly 25% of the patient sample had experienced violent victimization over the prior 6 months—a prevalence rate 11.8 times higher than that found in the NCVS comparison data (Teplin et al., 2005). The study’s reported prev- alence ratios did vary by type of violent victimization; however, the patient sample experienced higher rates of victimization across all cat- egories and subcategories of violence, including rapes and sexual assaults, robberies, and physical assaults. Recent examinations of data from the MacArthur Violence Risk Assessment Study have also found high rates of violent victimization (Monahan et al., 2017; Teasdale, 2009). In fact, researchers have reported a 43% past-year prevalence of violent victimization among this population (Monahan et al., 2017). Thus, across a range of samples and contexts, individuals with serious mental illness are significantly more likely than others to experience violent victimizations.

What still remains to be explained is why these victimizations occur. Several recent studies have attempted to fill this gap by examining the role clinical factors play in this increased victimization risk (Latalova et al., 2014; Maniglio, 2009). These studies have identified comorbid alcohol and substance use problems (Hiday et al., 1999; Langeveld et al., 2018; Marley & Buila, 2001) as correlates of victimization among people with severe mental illness. Some research has also found that younger age at first psychiatric hospitalization and a recent history of hospitalization substantially increase risk for violent victimization among both men and women (e.g., Goodman et al., 2001). Taken together, these findings suggest that certain factors, specifically those that are associated with increased disorder-related symptomatol- ogy, may play a role in increasing risk for violent victimization. This may be especially true in instances where disorder-related behavior is poorly understood by bystanders or is interpreted as cues of danger- ousness (Pescosolido et al., 1999).

Researchers have also explored whether more general risk factors are related to violent victimization among people with mental illness. Recent homelessness has been implicated as a risk factor for violent victimization both in the general population (e.g., Lee & Schreck, 2005)

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and among this population (Crisanti et al., 2014; Goodman et al., 2001;

Hiday et al., 1999; Maniglio, 2009; Teasdale, 2009). This is unsurpris-

ing, as homelessness reduces guardianship (i.e., the absence of a capable

person or entity who can prevent criminal offenses against a person or

property; see Cohen & Felson, 1979; Fitzpatrick et al., 1993) and is

strongly correlated with other risk factors for victimization, such as

substance use (e.g., Lee & Schreck, 2005). Researchers have also con-

sistently demonstrated the significance of prior crime and violence per-

petration as a significant risk factor for victimization among disordered

populations (Crisanti et al., 2014; de Waal et al., 2018; Langeveld et al.,

2018; Latalova et al., 2014; Maniglio, 2009; Policastro et al., 2016;

Teasdale, 2009). As some research suggests, this association may be a

function of conflicted social relationships with others (Silver, 2002). Although this literature has identified a number of risk factors asso-

ciated with violent victimization among individuals with serious mental

illness, which may be necessary to control for in subsequent analyses

of violent victimization, it is still unclear whether and in what ways

stigma affects victimization, net of these other factors. We now turn

to an examination of stigma and its potential for understanding

victimization.

Stigma, Mental Illness, and Negative Consequences

Stigma has been conceptualized in numerous ways including a discred-

iting attribute (Goffman, 1963) or elements consisting of labeling or

stereotyping (Link & Phelan, 2001). As stated previously, scholars

have argued that cultural stereotypes play an important role (Link &

Phelan, 2001), especially in the context of people with mental illness.

For example, socialization within our culture creates a set of beliefs

about mental illness early in life (Link et al., 1989). These beliefs, in

turn, help formulate individuals’ conceptions about what it means to

have a mental illness and the behaviors associated with people suffering

from mental illnesses (Link et al., 1989; Link & Phelan, 2001).

Unfortunately, a popular cultural stereotype of people with mental ill-

ness may include failing to fulfill societal obligations (Gerbner et al.,

1981) or being viewed as a danger to others, violent, or frightening

(Phelan et al., 2000). Therefore, people may avoid individuals with

mental illness in the context of employment, neighborhoods, intimate

partner relationships, and so on (Link & Phelan, 2001). This avoidance

may influence people with mental illness to believe that they will be

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devalued or discriminated against, thereby affecting their interactions

with others (Link et al., 1989). Feeling devalued or discriminated against can have negative conse-

quences for people with mental illness. In fact, several negative conse-

quences of this process have been identified in literature. For instance,

Link and colleagues (1989) found that patients who perceived that they

were devalued due to their status or stigma as a patient had constricted

social networks. In addition, scholars have also found that stigma neg-

atively affects the self-esteem of a person with mental illness (Link et al.,

2001; Livingston & Boyd, 2010; Verhaeghe et al., 2008). Researchers

have also found that stigma negatively affects the well-being and help-

seeking behaviors of a person with mental illness (Cruwys &

Gunaseelan, 2016; Link et al., 1997; Reynders et al., 2014) and can

contribute to suicidality (Rüsch et al., 2014). Given that stigma increases negative perceptions of one’s self and

negative interactions with others, it is possible that stigma leads to dis-

advantaged circumstances in general. That is, stigma may result in a

person with mental illness living in a disadvantaged neighborhood,

lacking gainful employment, and lacking meaningful relationships

with others. Although speculative, these disadvantaged circumstances

caused by stigma may influence another negative outcome: victimiza-

tion among people with mental illness.

Stigma, Mental Illness, and Victimization

We speculate that stigma and victimization may correlate for several

reasons. First, it is possible that the stigma of mental illness may influ-

ence a lack of capable guardianship (see Cohen & Felson, 1979).

Research has shown that the general public desires social distance

from people with mental illness because of the belief that this popula-

tion is dangerous (Link et al., 1999) or violent (Pescosolido et al., 1999).

Because of this perception, people with mental illness may have con-

stricted social networks (Link et al., 1989) or may be involved in con-

flicted social relationships (Silver, 2002). If people with mental illness

have constricted social networks and lack quality relationships because

of the stigma of having a mental illness, it is possible that this popula-

tion also lacks capable guardians who could prevent a victimization

experience. Second, the stigma of mental illness may lead to reduced well-being

(Link et al., 1997) and lower self-esteem (Verhaeghe et al., 2008), which,

in turn, may lead to the involvement in risky situations. It is plausible

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that people with mental illness may have maladaptive coping strategies

such as substance abuse (Ryan et al., 2014) that may lead this popula-

tion to find themselves in situations that are conducive to victimization

experiences. Alternatively, it is also possible that stigma associated with the expe-

rience of mental illness symptoms could increase offender motivation.

That is, if a person with mental illness acts bizarrely or triggers fear or

anxiety in people they encounter, it is possible that individuals may

preemptively defend themselves against a person perceived to be a dan-

gerous person, ultimately victimizing them to prevent their own hypo-

thetical, expected victimization. Osgood and colleagues (1996) argue

that motivation resides not in the individual rather within the situation.

It is possible that situational encounters may create offender motivation

(in this case, self-protection) in individuals who hold stigmatizing beliefs

about people who suffer from mental illnesses. Finally, it is also possible that the stigma of mental illness may lead

to disadvantaged situations that are conducive to victimization experi-

ences. As mentioned previously, people may avoid individuals with

mental illness in the context of employment, neighborhoods, or intimate

partner relationships (Link & Phelan, 2001). Given that stigma may

contribute, in part, to where a person with mental illness may live, it

is possible that stigma may lead to this population residing or working

in socially disorganized neighborhoods. In fact, Silver (2000) found that

compared with the general population, patients discharged from a

public psychiatric hospital resided in more disadvantaged and less

safe neighborhoods.

Current Study

Although it is possible that stigma may contribute to victimization expe-

riences through any or all the mechanisms just identified at this time a

lynch-pin finding remains unexplored—we do not know whether indi-

cators of stigma are associated with victimization. Thus, the purpose of

the current study is to examine if stigma and victimization are associat-

ed, and in what direction. In other words, does perceived stigma lead to

later victimization? Or does victimization lead to later perceived stigma?

To investigate these questions, we use the Community Outcomes of

AOT data. There are several benefits to using the AOT data. For

instance, given the longitudinal structure of the data, we are able to

assess victimization at follow-up waves, while controlling for stigma

and other control variables at baseline to establish temporal order. In

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addition, because AOT gathered data on a multitude of clinical and

general risk factors, we are able to assess the relationship between

stigma and victimization in the context of known risk factors for vic-

timization for this population. An additional benefit of the longitudinal

data collection is that we can assess the possibility of reciprocal causa-

tion. That is, although we have theorized that stigma could lead to vic-

timization, it is also possible that victimization leads to a person feeling

more stigmatized. Given that multiple waves of data were collected from

each participant and that both stigma and victimization were assessed at

each wave, we can estimate models to see if stigma at an earlier wave

predicts victimization at a later wave (holding constant victimization in

the earlier wave) or if victimization in an earlier wave predicts stigma in a

later wave (holding constant stigma in the earlier wave).

Method

Sampling

The Community Outcomes of AOT is a longitudinal study of 184 people

with serious mental illness (Link et al., 2011; Phelan et al., 2010).

Participants were selected from treatment facilities in New York and

were between the ages of 18 and 65 years (Phelan et al., 2010). Two

groups were utilized in the study, including a court-ordered assisted

outpatient group (n¼ 76) and an outpatient comparison group of indi-

viduals recently discharged from a psychiatric facility (n¼ 108; Phelan

et al., 2010). Once informed consent was obtained, interviews were con-

ducted every 3 months for a year (Phelan et al., 2010). Approximately

40% of the sample was diagnosed with schizophrenia spectrum disor-

ders, 32% was diagnosed with schizo-affective disorders, 7% was diag-

nosed with major depressive disorder, 19% was diagnosed with bipolar

disorder, and 2% was diagnosed with substance-induced disorders. For

a detailed account of the sampling procedures utilized in the Community

Outcomes of AOT study, see Phelan and colleagues (2010), Link and

colleagues (2008), or Link and colleagues (2011).

Measures

Dependent variable

Violent victimization. Following the MacArthur violence scale (see

Monahan et al., 2001), there are eight categories of violent victimiza-

tion, including (a) having had something thrown at the participant; (b)

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being pushed, grabbed, or shoved; (c) being slapped; (d) kicked, bitten, or choked; (e) being hit with a fist or beaten; (f) being physically forced to have sex against his or her will; (g) being threatened with a knife, gun, or weapon; and (h) having someone fire a gun, use a knife, or weapon on the participant (Link et al., 2011; Phelan et al., 2010). If an individual experienced one of these acts of violence after baseline, the participant was coded as a victim resulting in a dichotomous indi- cator of violent victimization (1) or not (0).

Independent variable

Perceived societal stigma. Perceived societal stigma was captured by asking participants a series of Likert-type questions to determine how strongly they felt devalued/discriminated against, different, or ashamed. Response options included strongly disagree, disagree, agree, or strong- ly agree. To create a stigma scale, an exploratory factor analysis (EFA) was conducted. EFA results identified three latent constructs. To avoid multicollinearity issues, only one of the scales identified through the EFA was utilized. Specific questions included in the scale are, “most people would accept a person who has been in a mental hospital as a close friend,” “most people believe that a person who has been hospi- talized for mental illness is just as trustworthy as the average citizen,” “most people would accept a person who has fully recovered from mental illness as a teacher of young children in a public school,” “most women would be willing to marry a man who has been a patient in a mental hospital,” or “most employers will hire a person who has been hospitalized for mental illness if he or she is qualified for the job.” Since the items showed acceptable reliability (Cronbach’s a¼ .74), a stigma measure was created by taking the mean of the five items at baseline. The resulting scale captures the perceptions that people with mental illnesses would not be accepted by most people.

Control variables

Violent victimization at baseline. Victimization reported at baseline is measured with the same items as the dependent variable and included as a control measure. If an individual reported any type of victimization in 1 month before the baseline interview, they are coded as (1), if they reported none of the victimizations, they are coded (0). This allows us to control for the effect of prior victimization on reported stigma.

Alcohol use at baseline. It is possible that alcohol use may lead to victimization. Thus, to test for this possibility, alcohol use is included as

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a control variable. The frequency of alcohol use (measured as � 10 days/month, 2–9 days/month, <2 days/month) is collapsed into a dichotomous indicator of any alcohol use (1) or not (0) at baseline.

Delusions at baseline. It is also possible that experiencing a delusion could lead to a victimization event (Goodman et al., 1997; Johnson et al., 2016; Teasdale, 2009; Walsh et al., 2003). To account for this possibility, delusions are included as a control variable. Delusions are assessed using the clinician-administered Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (DSM; that is, SCID; First & Gibbon, 2004). Specifically, participants were asked questions that assessed the presence or absence of delusions within the past 3 months. To control for delusions that may influence victimiza- tion (i.e., paranoid, persecutory, delusions of control, and thought broadcasting), the following questions are utilized: “has it seemed like people were talking about you or taking special notice of you?” “what about anyone going out of their way to give you a hard time or trying to hurt you?” “did you ever feel that your actions were being controlled or directed by some other external force?” and “did you feel as if your thoughts were being broadcast out loud so that other people could actually hear what you were thinking?” If an individual reported one of these delusions at baseline, the participant is coded as experiencing delusions (1) or not (0).

Violence at baseline. Eight categories of violent behavior are utilized, including (a) throwing something at someone; (b) pushing, grabbing, or shoving someone; (c) slapping someone; (d) kicking, biting, or choking someone; (e) hitting someone with a fist or beating someone up; (f) physically forcing someone to have sex against his or her will; (g) threat- ening someone with a knife, gun, or weapon; and (h) firing a gun, using a knife, or weapon on someone (Link et al., 2011; Phelan et al., 2010). If an individual reported one of these experiences at baseline, the partic- ipant is coded as violent resulting in a dichotomous indicator of violent behavior (1) or not (0).

Perceived coercion. Perceived coercion is assessed through a modi- fied version of the MacArthur Perceived Coercion scale (Cronbach’s a¼ .86; Gardner et al., 1993), which captures if the respondent felt free to choose to be in outpatient treatment (Link et al., 2008). Examples include statements such as “it was your idea to get mental health treatment” or “you had control over getting mental health

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treatment.” Response options ranged from strongly agree (0) to strong-

ly disagree (3). Thus, the perceived coercion scale is created by taking

the mean of the six items at baseline.

Number of involuntary hospitalizations. The number of involuntary

hospitalizations is measured by asking how many times the respondent

had been hospitalized against their will at baseline. The natural log is

taken to account for the skewness of the variable, with higher numbers

representing a greater number of involuntary hospitalizations.

Site. To control for the site from which the respondent was recruited

from, a site control measure is included. Specifically, three hospitals

participating in the study are utilized. To maintain confidentiality, we

have labeled and created a series of dummy variables of these sites as (a)

Site 1, (b) Site 2, and (c) Site 3. Site 2 is the excluded referent category.

Age. The age in years of the respondent is included at baseline.

Sex. A dichotomous variable of sex at baseline is included (i.e., male

[1] and female [0]).

Social desirability. Social desirability is captured through a 15-ques-

tion version of the Crowne–Marlowe scale (Cronbach’s a¼ .68; Crowne

& Marlowe, 1960). Examples include “never hesitating to help some-

one,” “never intensely disliked anyone,” or “always willing to admit

mistakes.” Thus, a social desirability measure is created by taking the

sum score of the 15 items at baseline.

Race. Three dummy variables, including Black, Hispanic, and

White/Other, are used to control for race at baseline with White/

Other as the reference category (only 32 subjects were code as White/

Other).

Data Analysis

Due to the design utilized in the AOT data (i.e., interviews were con-

ducted every 3 months for a year), we were able to lag waves to address

temporal ordering. The dependent variable is measured as any victim-

ization occurring between Month 1 and Month 12 after baseline, while

all the independent variables are measured temporally prior (at base-

line), including stigma and prior victimization. In addition, because

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logistic regression takes into account the dichotomous nature of the dependent variable (i.e., victim or not), a logistic regression model was employed (Hosmer & Lemeshow, 2000).

Because the participants were interviewed every 3 months for 1 year, there were some participants who did not report data at each wave. We chose multiple imputations to address missing data for several reasons. First, because the full sample can be retained, statistical power is increased. Second, Schafer and Graham (2002) highlight that multiple imputation is a more efficient missing data technique than case deletion because there are no units that are sacrificed. Third, the missing values are predicted from each of the participant’s previous observed values (Schafer & Graham, 2002). Multiple imputation approaches have been described extensively elsewhere (Rubin, 1987; Schafer & Graham, 2002; Sinharay et al., 2001). Briefly, the approach uses a regression-based estimate for item-level missingness, under a missing-at-random assump- tion. That is, we assume that the data are missing at random, condi- tional on the covariates included in the model, but that it is not dependent on the actual value of the missing data. The approach gen- erates multiple data sets that contain complete data with multiple plau- sible values for the missing data. Considering that Graham and colleagues (2007) suggest that 40 imputed data sets can remove noise from statistical summaries, we utilized 40 imputed data sets that were pooled together to produce results using Rubin’s rules (Rubin, 1987).

Results

Sample Description

As shown in Table 1, approximately 57% reported that they had been violently victimized in 1 month prior to baseline. Over the course of the follow-up interviews, 18% reported violent victimization. The mean age of the participants was 37 years, and approximately 40% were females. Among the site locations, 38% of the participants were at Site 2, 35% at Site 3, and 26% were at Site 1. On average, the mean level of perceived stigma was 1.57 (on a scale that ranged from 0.00 to 3), indicating that the majority of the sample had moderate levels of perceived stigma. In addition, 31% of the participants had consumed alcohol in 1 month prior to baseline. The average level of social desirability was 8.49, on a scale ranging from 0 to 15, and the average level of perceived coercion was 1.37, on a scale ranging from 0 to 3. Finally, the average number of involuntary hospitalizations was 1.05. Finally, approximately 54% of

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the participants were Black, 29% were Hispanic, and 17% were

reported as Other, which included White.

Bivariate and Multivariate Analysis

As shown in Table 2, only three measures were significantly correlated

with violent victimization in the bivariate analysis. Specifically, the var-

iables, perceived stigma, prior victimization at baseline, and delusions

were significantly correlated with violent victimization in 1 year after

baseline, in the bivariate analysis. In the multivariate analysis of Table 2, the same three variables also

predicted violent victimization, controlling for the other variables in the

model. As hypothesized, for individuals with higher perceptions of

stigma, the odds of being victimized significantly increased. That is,

for every one-point increase in perceived stigma, the odds of being vic-

timized were approximately three times higher. To illustrate, as shown

in Figure 1, people who are one to two standard deviations below the

Table 1. Sample Description (N¼ 184).

Variable M SE Range

Dependent variable

Victimization 0.18 .029 0 to 1

Independent variable

Stigma 1.57 .040 0.00 to 3.00

Control measures

Alcohol use 0.31 .035 0 to 1

Delusions baseline 0.65 .035 0 to 1

Violence baseline 0.31 .035 0 to 1

Victimization baseline 0.57 .037 0 to 1

Perceived coercion 1.38 .045 0 to 3

Involuntary hospitalization 1.05 .068 0 to 2.71

Site 2 0.38 .036 0 to 1

Site 1 0.27 .033 0 to 1

Site 3 0.35 .035 0 to 1

Age 37.00 .819 18 to 64

Male 0.59 .037 0 to 1

Social desirability 8.49 .234 �3.01 to 21.99

Black 0.54 .037 0 to 1

Latino 0.29 .034 0 to 1

Other 0.17 .028 0 to 1

Note. Mean, standard error, and range reported from the pooled imputation model.

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mean of perceived stigma have a 10% to 20% predicted probability of

experiencing violent victimization. In contrast, among people who are

one to two standard deviations above the mean of perceived stigma, the

predicted probability of violent victimization doubles. That is, the pre-

dicted probability of experiencing violent victimization for people one

standard deviation above the mean level of stigma is approximately

40%, while the predicted probability of experiencing victimization is

above 50% for people who are two standard deviations above the

mean level of stigma. As can be seen, there is a clear pattern of victim-

ization risk increasing as perceived stigma increases. As shown in Table 2, in addition to perceived stigma, for people who

have been victimized at baseline, the odds of being victimized at the

follow-up waves significantly increase. Specifically, baseline victims had

3.79 times the odds of being victimized during the 1-year follow-up,

compared with baseline nonvictims. This is unsurprising, given the lit-

erature showing that victimization can also lead to an increased risk of

subsequent victimization, known as recurring victimization (see Fisher

et al., 2010; Tseloni & Pease, 2003; Turanovic & Pratt, 2014). Finally,

Table 2. Results From Logistic Regression Predicting Victimization (N¼ 184).

Variables

Bivariate Multivariate

b SE B SE

Partial

Odds Ratios

Perceived stigma 1.064** .41 1.137* .50 3.12

Delusions 1.186* .52 1.198* .57 3.31

Alcohol use 0.085 .44 �0.400 .53 .67

Violence 0.986* .42 0.711 .49 2.04

Victimization baseline 1.572** .51 1.332* .57 3.79

Perceived coercion 0.135 .32 �0.210 .39 0.81

Involuntary Hospitalization �0.169 .31 �0.134 .39 0.87

Site 1a 0.204 .45 �0.098 .59 0.91

Site 3a �0.604 .45 �0.491 .57 0.61

Age 0.018 .02 0.016 .02 1.02

Male �0.024 .41 �0.401 .51 0.67

Social desirability 0.010 .07 �0.015 .08 0.98

Blackb �0.396 .40 �0.315 .59 0.73

Latinob 0.151 .43 �0.225 .67 0.80

aSite 2 is the excluded reference category. bOther race is the excluded reference category. *p< .05. **p< .01.

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among people who were experiencing delusions, the odds of being vic-

timized significantly increased by 3.31 times during the 1-year follow-

up, compared with people who were not experiencing delusions. Surprisingly, however, victimization at baseline does not predict per-

ceived stigma at the following wave. As shown in Table 3, victimization

at baseline and perceived stigma at the following wave are not signifi-

cantly associated in the multivariate analysis. This suggests that some

mechanism in feeling stigmatized influences violent victimization of

individuals with mental disorders, but being victimized does not predict

perceived stigma in later waves (i.e., follow-up period).

Discussion

There is relatively little known about if and how a person with mental

illness’s own perception of stigma may affect victimization experiences.

Although we have speculated about some potential mechanisms that

may explain why stigma and victimization correlate, it is first necessary

to determine if perceived stigma is related to victimization and in what

direction. Therefore, the purpose of the current study was to fill this gap

by investigating that possibility.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

2 Standard Devations

Below

1 Standard Devation

Below

At the Mean of Stigma

1 Standard Devation

Above

2 Standard Devations

Above

Predicted Probability of Victimization

Figure 1. Predicted probability of victimization. Note. All other variables in the model were held constant at their mean (continuous variables) or mode (dichotomous variables), when calculating the predicted proba- bility of victimization conditional on the stigma level.

Harris et al. 15

Harris et al. NP1241

Results from this study suggest that perceived stigma and victimiza- tion are significantly associated and that perceived stigma actually increases the odds of experiencing a victimization event threefold. In other words, findings from our study suggest that the stigma attached to mental illness (i.e., being dangerous, violent, undesirable) is a statisti- cally significant predictor of victimization experiences.

Results from this study also demonstrated that victimization at base- line did not predict perceived stigma at the follow-up wave (see Table 3). Although it could be assumed that a victimization event would further exacerbate one’s perception of stigma, the current study’s results did not support this assumption. It may be that (because most people are victimized by nonstrangers) the victimization feels per- sonal (for reasons other than mental illness–related processes). Thus, it does not alter general perceptions of how most people feel about the disordered population (as captured by our stigma measure). Rather, they are able to explain away the victimization event using other explanations (conflicted social relationships; see Silver, 2002). Consequently, stigma remains unchanged. In contrast, those who expe- rience more stigma are at an increased risk of later victimization.

Table 3. Results From Ordinary Least Squares Regression Predicting Stigma at 3 Months (N¼ 184).

Variables b SE

Victimization baseline 0.085 .08

Delusions �0.129 .08

Alcohol use �0.033 .09

Violence 0.167 .09

Stigma baseline 0.387*** .08

Perceived coercion 0.064 .07

Involuntary hospitalization 0.106 .01

Site 1a 0.100 .10

Site 3a 0.221** .09

Age 0.004 .00

Male 0.015 .08

Social desirability 0.000 .02

Blackb �0.026 .12

Latinob �0.072 .14

aSite 2 is the excluded reference category. bOther race is the excluded reference category. *p< .05. **p< .01.

16 Journal of Interpersonal Violence 0(0)

NP1242 Journal of Interpersonal Violence 37(3-4)

Importantly, the current study controlled for a variety of variables

that are significantly correlated with victimization. We did find that

prior victimization events and experiencing delusions increased the

odds of experiencing a victimization event. Given that prior victimiza-

tion (Teasdale et al., 2014) and experiencing delusions (Goodman et al.,

1997; Johnson et al., 2016; Teasdale, 2009; Walsh et al., 2003) are

established predictors of victimization among people with mental ill-

ness, this is unsurprising. What is surprising, however, is that despite

controlling for these variables, the relationship between perceived

stigma and victimization remained unchanged. Following from this finding, a natural next line of inquiry is under-

standing why such a relationship exists. Some contributing mechanisms

that may be important to examine in future research include if and how

disadvantaged situations, reduced guardianship, and reduced well-being

contribute to the relationship between stigma and victimization because

these may serve as potential mediating mechanisms. Future pursuits should also investigate where stigma belief structures

are learned. We suspect that people with mental disorders learn these

beliefs from the stigmatizing attitudes of those around them. Small

group social learning may be the culprit in creating these belief struc-

tures, and those who help create these stigmatizing beliefs may be the

same individuals who are victimizing them. Future research should take

a social networks approach to the study of stigma and victimization to

test this assertion. It is also possible that individuals who perceive them-

selves to be stigmatized alter their activity patterns in ways that make

them vulnerable to victimization. One such activity pattern may be

engaging in risky behaviors such as alcohol use. Although we did not

find a connection between alcohol use and victimization in this sample,

it is fairly commonly connected to victimization in other research

(Mustaine & Tewksbury, 1998), and those who are more stigmatized

may turn to alcohol as a coping mechanism, setting themselves up for

increased victimization risk. It is possible that the relatively low rates of

alcohol use among this sample may be due to the outpatient treatment

the individuals were receiving.

Limitations

As with any study, there are several limitations that should be

addressed. First, because it was impossible to assign participants ran-

domly to varying stigma levels, it is possible that we did not measure a

third variable that could affect both stigma and victimization. We do

Harris et al. 17

Harris et al. NP1243

address this possibility, however, by controlling for possible confound- ing variables that have been shown to influence victimization, including prior victimization events. Ultimately, like all survey research, we esti- mate an association, one that might be spurious due to some unmeas- ured factor. Thus, we caution that these results may not be causal. We are, however, bolstered in our expectation that this relationship is reli- able, in that a confounder would simultaneously need to cause the perception of stigma in the victim and also the victimizing behavior of a perpetrator. It is unclear what that third variable could be that would cause these two processes in separate individuals. Moreover, the stability of the association, despite the controls we did include, suggests that it is robust to model specifications.

Our measure of stigma includes five items examining the partici- pants’ perceptions of stigma. Although these items were useful for the purpose of the current study, future research should examine expanded measures of perceived stigma. Relatedly, our measure of stigma cap- tures one’s perceived stigma but does not capture the perceptions of individuals around the participants (i.e., the stigma attached to individ- uals with mental disorders by others). Although we suspect that these beliefs are learned from those in small groups surrounding the respon- dent, it is possible that they were learned elsewhere, from the media for example. It would be useful for future studies to incorporate a social networks approach with such measures to estimate the contribution to victimization of these stigmatizing beliefs from others in the individual’s social network.

The measure of violent victimization consisted of eight behaviors ranging from having something thrown at the participant to having someone use a weapon against the respondent. Consequently, this mea- surement lumped in several categories that varied in the severity of victimization. Thus, by dichotomizing violent victimization, the current study could underestimate the impact of stigma on victimization sever- ity. However, the low rate of involvement in victimization prevents studying variation in severity with the current data.

In addition, alcohol use was measured by collapsing frequency of use into a dichotomous measure of any alcohol use. While this measure captures usage, it fails to assess frequency, duration, and severity of use. It may be useful for future research to parse out the extent to which alcohol contributes to victimization by incorporating measures that extend beyond simply engaging in drinking alcohol and, instead, incorporating other measures that establish frequency, duration, and severity of use. The low rate of involvement in alcohol usage prevented

18 Journal of Interpersonal Violence 0(0)

NP1244 Journal of Interpersonal Violence 37(3-4)

us from examining the severity of use within the current data. Similarly,

delusions were measured by assessing their presence, but not their inten-

sity or frequency. It would be useful to future research to include meas-

ures of psychotic symptomatology that captures intensity and frequency

to examine if and how such measures contribute to victimization or

perceptions of stigma.

Conclusion

Despite these limitations, this study has important implications for clin-

ical practice and informing literature. Indeed, this was one of the first

studies to begin to answer Teasdale’s decade-old call to assess the role

of stigma in victimization among individuals with mental disorders

(Teasdale, 2009). Although the current study’s results suggest that

stigma plays a role in the victimization experience among this popula-

tion, there are still several unanswered questions. For instance, what are

the mediators of this association that may help us understand why

stigma leads to victimization experiences among individuals with

mental disorders? For example, future research may want to investigate

if and how nonviolent social cues or negative emotions may function as

mediators in the relationship between stigma and victimization. In other

words, it is possible that nonviolent social cues may be a byproduct of

feeling stigmatized, which could then lead to a victimization experience.

Similarly, negative emotions may also be another consequence of

stigma, ultimately leading to victimization. For example, it is possible

that perceptions of stigma can produce negative emotions. Negative

emotions, in turn, may influence a person to behave in ways that are

provocative and increase their involvement with conflicted social rela-

tionships (Silver, 2002), which may ultimately lead to a victimization

event. Future research should explore these potential mediators and

possibilities. In terms of clinical practice, clinicians may want to be particularly

sensitive to an individual’s perceptions of the stigma associated with his

or her mental illness. Clinicians may want to instruct their clients on

target-hardening strategies to adjust their perceptions of stigma, which,

in turn, may reduce their victimization risk. We suggest nurturing social

relationships with individuals who may serve as guardians is one such

target-hardening strategy and is consistent with life skills training

programs. Finally, this study has important implications in informing the

stigma literature regarding people with mental illness. A number of

Harris et al. 19

Harris et al. NP1245

harmful outcomes related to stigma have already been identified in lit-

erature in this population. Results from this study suggest that victim-

ization is another such harmful outcome, further illustrating the

importance of addressing stigma-related processes. Furthermore, this

study represents one of the initial efforts at understanding mental illness

as a form of diversity in connection to stigma and victimization.

Although diversity is often understood in terms of issues related to

gender, race, class, and sexual orientation, mental health is also an

important way in which individuals stigmatize or engage in othering

(Goffman, 1963). In other words, mental illness is an important signifier

of difference (stigma). Focusing on stigma, as we have done in the

current study, is consistent with literatures on other traditionally

focused studies of diversity such as sexual orientation, gender, and

race. Perhaps by addressing stigma-related processes among this pop-

ulation through clinical practice and future research, an indirect benefit

will be reducing a number of harmful outcomes, including

victimization.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the

research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or

publication of this article.

ORCID iD

Michelle N. Harris https://orcid.org/0000-0001-7412-6894

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Author Biographies

Michelle N. Harris is a doctoral candidate at Georgia State University in the Department of Criminal Justice and Criminology. She holds a BA in psychol- ogy and criminal justice from the University of Arkansas and an MS in criminal justice and criminology from Georgia State University. Her research interests include mental health as it relates to criminological processes and victimology. Her recent work appears in Deviant Behavior, Journal of Interpersonal Violence, European Journal of Criminology, and Journal of Criminal Justice.

Miranda L. Baumann is a doctoral candidate in the Department of Criminal

Justice and Criminology at Georgia State University. She graduated from Georgia State University with a BA in political science, a BS in criminal justice, and an MS in criminal justice and criminology. Her research focuses on violence committed by and against people with serious mental illness, the relationship between substance use and violence, and gun violence. Her work has been published in outlets including the Journal of Interpersonal Violence, the International Journal of Law and Psychiatry, and Pearson Education. She has been teaching at Georgia State University for 3 years, during which time she has taught statistical analyses in criminal justice, criminological theory, race in the criminal justice system, and American criminal courts.

Brent Teasdale is a professor and the Department Chair in Criminal Justice Sciences. He received his PhD in sociology from the Pennsylvania State University. His scholarship focuses on advanced quantitative methods, violence by and against people with mental disorders, and substance abuse prevention. His published work has appeared in outlets such as Criminal Justice & Behavior, Journal of Interpersonal Violence, Journal of Quantitative Criminology, Justice Quarterly, Prevention Science, and Social Problems. He has been recognized in several publications ranking the best criminologists in the United States (Copes et al., 2012; Walters, 2015; Khey, 2017).

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NP1252 Journal of Interpersonal Violence 37(3-4)

Bruce G. Link earned his BA from Earlham College and MS and PhD degrees from Columbia University. His interests are centered on topics in psychiatric and social epidemiology. He has written on the connection between socioeco- nomic status and health, homelessness, violence, stigma, and discrimination. Currently he is conducting research aimed at understanding health disparities by race/ethnicity and socioeconomic status, the consequences of social stigma for people with mental illnesses, and the connection between mental illnesses and violent behaviors.

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