Mental Illness and Racial Disparities in Correctional Staff-Involved Violence: An Analysis of Jails in the United States

Abstract

In this study, we draw on theories of opportunity and focal concerns to examine how mental illness and race correspond to correctional staff-involved violence, particularly within the transient and diverse context of jails. Using a sample of 3,936 people incarcerated in jails from the 2011 to 2012 National Inmate Survey (NIS-3), we analyze how an individual’s mental health status (number of mental illness diagnoses) and race relates to the risk of staff-inflicted victimization. Using a series of Firth’s logistic regression models, we find that Black and Hispanic individuals are much more likely to be victims of correctional staff assault than their White counterparts. Those with one and two or more mental illness diagnoses are also at greater risk for staff-involved violence, respectively. Supplementary models show that those who suffer from serious mental illness are at particularly high risk for staff victimization. In a subsequent moderation analysis, we find that race does not condition the influence of mental health diagnoses on risk for staff-involved violence. Our findings reinforce the need for more research on correctional staff-involved violence and the implications of this research support calls for enhanced training of correctional staff regarding mental illness and racial bias.

Keywords

criminology mental health and violence workplace violence race

High-profile cases of the use of force by law enforcement against Black Americans, including George Floyd, Daunte Wright, Walter Wallace Jr., and Breonna Taylor, have led to increased calls for addressing violence by law enforcement officials. Although the precise nature of the relationship between race and police use of force remains under debate (Hollis & Jennings, 2018; Shjarback & Nix, 2020), numerous studies have found that police officers are more likely to use force and take lethal action against Black and Indigenous People of Color (BIPOC) than their White counterparts (Bolger, 2015; Edwards et al., 2019; Kramer & Remster, 2018; Paoline et al., 2018). In addition to race, scholars have identified mental illness among those apprehended by the police as a key risk factor related to use-of-force decisions (Bolger, 2015; Engel et al., 2000; Morabito et al., 2017; Rossler & Terrill, 2017). Yet despite a growing body of research on the intersection of race, mental health, and the use of force by the police, police officers represent just one of the many criminal legal actors with whom individuals may come into contact. Relatively little is known about the factors that influence violence involving other criminal legal employees, such as correctional officers (COs) and other non-custody staff (Hogan et al., 2005; McNeeley, 2021; Wolff et al., 2007). Of the limited studies on correctional staff violence, none examine the issue among jailed populations.

Jails in the U.S. house particularly transient, racially diverse, and highly vulnerable individuals who are significantly more likely to suffer from physical ailments, mental illness, and substance use disorders than the general population (Greifinger, 2007). In 2019, U.S. jails held approximately 734,500 individuals with a weekly population turnover rate of 53% and an average stay of just 26 days (Zeng & Minton, 2021). Scholars estimate that about 25% of those incarcerated in jail have a mental health problem (Bronson & Berzofsky, 2017; Semenza & Novisky, 2020) and roughly 65% are candidates for substance abuse treatment (National Center on Addiction and Substance Abuse at Columbia University, 2010). Researchers often link mental illness or substance abuse disorder to an individual’s risk of violence involvement while incarcerated due to perceived vulnerability and antagonistic behavior (e.g., Ellison et al., 2018; Grosholz & Semenza, 2021; Pare & Logan, 2011; Wolff et al., 2009). However, most of this research focuses on violence perpetrated against incarcerated persons by other incarcerated persons, rather than violence against incarcerated persons by correctional staff.

Staff-involved violence in carceral settings has been strikingly understudied. This study is therefore the first to examine the problem of staff-perpetrated violence using a nationally representative sample of people incarcerated in jails. Using data on 3,936 incarcerated people in jails from the 2011 to 2012 National Inmate Survey (NIS-3), this study extends the literature in three important ways. First, we draw on theories of opportunity and focal concerns perspectives to discuss how mental illness and race influence staff-involved violence while incarcerated due to perceived vulnerabilities and antagonistic characteristics. Second, we empirically assess how an individual’s mental health status, in conjunction with race, influences correctional staff violence against incarcerated persons. This approach differs from prior work in that we focus on staff-inflicted violence rather than violence inflicted by incarcerated persons themselves. Finally, we consider here the particular carceral context of jails, rather than prisons, to address why these settings may be especially salient for the dynamics of mental health, race, and correctional staff violence examined here. These contributions highlight the need for additional empirical work and policy development on staff-inflicted violence in jails.

Literature Review

Correctional Staff Violence

There are more than 400,000 COs employed in prisons and jails throughout the U.S., in addition to the many non-custody staff that supports correctional supervision (Bureau of Labor Statistics, 2021). These officers are responsible for maintaining order in correctional facilities while enforcing institutional conduct. There remain very limited national data on rates of physical violence committed by correctional staff against incarcerated people and much of the research on this issue relies on limited samples. For instance, Wolff et al. (2007) estimated the prevalence of staff-inflicted violence ranged from 83 to 321 per 1,000 incarcerated people using a sample of roughly 8,000 men and women in 14 prisons throughout a single mid-Atlantic state. In another study of 500 incidents among male prisoners in a maximum security facility in Minnesota (McNeeley, 2021; McNeeley & Donley, 2021), approximately 19% of respondents reported experiencing physical use of force by a correctional employee. As we show in our results, we find that about 5% of respondents in the final sample report staff violence victimization in jails. The risk of staff victimization may therefore be highly variable depending on the sample or particular correctional facility in question. Knowledge about staff violence against those in jails may be further obfuscated by underreporting due to fear of retaliation from COs or lack of data collection by correctional administrators (Department of Justice, 2020).

Although it remains difficult to estimate the general prevalence of staff violence in jails, recent qualitative research provides important insight into the situational dynamics of this problem. For instance, in a study conducted on prison staff misconduct in Ohio, Novisky et al. (2022) found that staff violence was the second most common type of staff misconduct experienced by formerly incarcerated persons, surpassed by only medical neglect. Among the 38 people interviewed, many described situations where a staff member’s use of force was excessive. Male respondents often detailed accounts of physical violence used against them while women’s descriptions of violence overwhelmingly focused on staff perpetration of sexual violence. Staff often restrained an incarcerated individual and “take it too far” by being excessively rough, using mace unnecessarily, or taking advantage of spaces lacking security cameras to hurt individuals without being caught (Novisky et al., 2022). In the study, respondents also recounted staff provoking violence between incarcerated persons as a form of particularly cruel indirect abuse. Western’s (2018) qualitative sample of returning citizens in Boston not only described prison violence as problematic but more than a quarter of the sample reported witnessing violence perpetrated by correctional staff. Similarly, Venters’ (2019) research offers additional qualitative evidence of abuse and neglect at the hands of correctional staff among those jailed at Riker’s Island in New York. Anecdotal evidence of correctional staff violence in places like the shuttered Edna Mahan Correctional Facility in New Jersey (Tully, 2021) and other correctional institutions throughout the country (The Marshall Project, 2021) provide additional, though not systematic, evidence of physical violence committed by staff against incarcerated people. Researchers have found that factors like race (McNeeley, 2021), gender (Wolff et al., 2007), and organizational climate (Griffin, 1999) all influence rates of staff violence in prisons and willingness to use force in local jails. However, research remains limited regarding the full range of risk factors that contribute to correctional staff violence.

A general opportunity framework is useful for examining factors that contribute to the risk of staff violence in jails. This perspective was first developed alongside routine activities theory to understand the factors that contribute to violent victimization in broader society (Cohen & Felson, 1979; Finkelhor & Asdigian, 1996; Hindelang et al., 1978). However, researchers have expanded this perspective to examine the likelihood of victimization while incarcerated since prison and jail environments represent high-risk contexts in which suitable targets often meet motivated offenders in the absence of capable guardians (Ellison, 2017; Ellison et al., 2018; Grosholz & Semenza, 2021; Wooldredge & Steiner, 2012, 2014). Wooldredge (2020) suggests that a general opportunity framework provides an appropriate way to understand violent victimization among people under correctional control by focusing on individual vulnerability and antagonism. In essence, the more vulnerable or the more antagonistic a potential victim is perceived by others, the higher their likelihood of victimization will be. Scholars argue that characteristics of incarcerated individuals contribute to heightened “target vulnerability” and/or “target antagonism” which ultimately increases the risk for violent victimization (DeLisi, 2019; Finkelhor & Asdigian, 1996; Garofalo, 1986; Wilcox et al., 2003).

Mental Illness in Jails and Implications for Violent Victimization

Mental illness is a key factor that corresponds to an incarcerated person’s risk of victimization because it can influence both perceptions of vulnerability and antagonism by other incarcerated persons and correctional staff (Blitz et al., 2008; Ellison et al., 2018; Grosholz & Semenza, 2021; Nettelbeck & Wilson, 2002; Wolff et al., 2007, 2009). For instance, mental illness may make one appear to be weak or an easy target for violence, resulting in the perception of greater vulnerability by those looking to do harm. When one appears weak, potential offenders—either other incarcerated individuals or correctional staff—may view the risk of retaliation to be low. Novisky et al. (2022), for example, found that formerly incarcerated women identified mental illness as a risk factor for staff-perpetrated sexual violence. Similarly, McNeeley (2021) found that individuals displaying symptoms of mental illness were more likely to be involved in a use of force incident including the use of pinion restraints, cell extraction, and physical force. On the other hand, mental illness may lead individuals to act out or behave in ways that are perceived as unpredictable by other incarcerated persons or staff. Those with mental illness may be seen by others as erratic or unintentionally aggressive while disrupting the status quo of the institution, angering others, and increasing the likelihood of violent interactions (Grosholz & Semenza, 2021; Irwin, 1985). Even if the individual poses no practical threat and intends no harm, the perception of weakness or threat may be enough to bring about a greater risk of violence.

The link between mental illness among incarcerated individuals and staff violence may be especially salient in jails. Since jails house far more transient populations than prisons, staff may be unfamiliar with the habits and needs of the incarcerated population, including the particularities of their medical conditions. Without proper knowledge of the mental illnesses that affect individual incarcerated people or the time to properly acclimate to the routines of those newly brought into the jail, staff may rely on incomplete information about an individual and mistake unusual behavior due to mental illness as threatening or antagonistic. This may lead to increased opportunities for violent altercations if the staff member feels like they must invoke aggression to maintain institutional order or protect themselves or others. Correctional staff may also simply interact more frequently with individuals suffering from mental illnesses, further increasing the risk of an altercation. Additionally, staff members may view individuals with a mental illness as weak and therefore an optimal target for violence. In any case, the institutional nature of jails may render mental illness an especially pertinent risk factor for violent victimization.

The Influence of Race on Staff-Involved Violence

Related to the dynamics of vulnerability and antagonism between incarcerated persons and correctional staff in jail settings, we argue that race can also influence the risk of staff-involved violence. Scholars often invoke focal concerns theory when studying police use-of-force (Johnson, 2005; Steffensmeier et al., 1998; Ulmer & Johnson, 2004), arguing that criminal legal actors’ decisions depend on three factors: blameworthiness, dangerousness, and practical constraints or consequences. Similarly, since the decisions of correctional staff must be made quickly considering situational circumstances and safety concerns, custody officers frequently “develop perceptual shorthands so they can use known information to fill in the gaps in their knowledge regarding those three factors. These perceptual shorthands can be influenced by stereotypical views regarding race and crime; for example, they may unconsciously consider nonwhite individuals more dangerous” (McNeeley, 2021, p. 4). Along these lines, we assert that staff may utilize shorthand strategies or heuristics when engaging with people of color in jail, especially when they are unfamiliar with many of those incarcerated due to the transiency of jailed populations. Thus, incarcerated persons of color may experience a greater risk for correctional staff-involved violence than their White counterparts.

Further, when disciplining incarcerated people with an unknown or misunderstood mental illness, correctional staff may become even more reliant on the heuristics that influence decision-making on the use of force or apprehending an incarcerated individual using violence. The shorthands employed by staff are therefore likely further informed by stereotypes and stigma associated with both mental health and race. Compared to their White counterparts, people of color have higher rates of severe mental illness (Centers for Disease Control and Prevention, 2016). Additionally, they are significantly less likely to report symptoms due to stigma from their communities (Avent Harris et al., 2020; Rastogi et al., 2012), are more likely to face institutional barriers to treatment (Greif Green et al., 2020), and have a greater risk for misdiagnosis and mistreatment due to racism within the mental health field (Gushue, 2004; Maura & Weisman de Mamani, 2017). When people of color do seek treatment, they are more likely to be labeled as dangerous compared to their White counterparts (Spector, 2001). Thus, mental illness far too often goes untreated or misdiagnosed for people of color, increasing their likelihood of mistreatment and stigma while concurrently heightening the risk for entanglement with the criminal legal system (Lee et al., 2017).

Although the general stigma around mental illness persists, people of color, particularly Black persons, are more likely to be perceived and treated as dangerous and threatening due to their mental illness while Whites are more likely to be approached with empathy (DeLuca et al., 2022). Research also finds those holding authoritarian views, which often include people employed in corrections (Stohr et al., 2012), are more likely to rely on stereotypes and stigmatize people of color with mental illness (DeLuca et al., 2022), further increasing the vulnerability of mentally ill incarcerated people of color. Thus, one’s race may influence the shorthand tactics staff engages in while interacting with incarcerated individuals with mental illness. Yet there has been little research to investigate how race might condition the influence of mental illness on risk for involvement in correctional staff violence. In one study of 500 incidents in a single prison in Minnesota from McNeeley (2021), the author did not find evidence of an interaction between race and mental health symptoms for the risk of use of force by correctional staff. However, more research is needed on the issue, especially in jails. We suspect that while all individuals with mental illness are at greater risk for violence from a correctional staff member while in jail due to heightened perceptions of vulnerability and/or antagonism, these associations may be conditioned by race such that people of color experiencing mental illness face a greater threat of violence involved with correctional staff. Given these expectations, we offer the following three hypotheses:

H1: Those with at least one mental illness diagnosis will be at greater risk for staff violence involvement while incarcerated than those without a mental illness diagnosis.

H2: Incarcerated people of color will be at greater risk for staff violence involvement than incarcerated Whites.

H3: Race will moderate the relationship between mental illness diagnosis and staff violence involvement among incarcerated people, such that those of color (e.g., Black, Hispanic) with at least one diagnosed mental illness will be at greater risk for violence involvement compared to their White counterparts.

Method

Data

Data for this study come from the 2011 to 2012 National Inmate Survey (NIS-3)—Jails. The data were collected between February 2011 and March 2012 by RTI International in cooperation with the Bureau of Justice Statistics (BJS). These are the most up-to-date, national-level data on jails in the U.S. Interviews were conducted using computer-assisted personal interviewing and audio computer-assisted self-interviewing data collection methods. On average, interviews lasted for 35 minutes. Paper surveys were provided to those in facilities without the capacity for private interviews. The use of these data was approved by IRB Proposal #2020001607 at Rutgers University.

The NIS-3 Jails survey consists of the main survey and an alternative survey, which includes in-depth questions on mental health, facility climate, and correctional staff interactions. Given the interests of the present study, we use data exclusively from the alternative survey. The main survey was conducted across 358 randomly selected jails, yielding 61,351 adult respondents. Of these respondents, 5,914 jailed participants were randomly selected to take the alternative survey and 5,494 eligible respondents completed enough of the survey to be included in the publicly available data file provided by the Inter-University Consortium for Political and Social Research.¹ In total, 3,936 people were asked questions across all of the survey modules included in our final models, resulting in our analytic final sample.²

Measures

We measure correctional staff-involved violence as our dependent variable using a binary item that asked respondents, “Have you been in a fight, assault, or incident in which a correctional officer or other facility staff person tried to harm you?” We coded responses as 0 for “no” and 1 for “yes.” Respondents were asked about these experiences of violence within the past year pertaining to their most recent admission to jail. Although it is not possible to know the precise details of the violent incident given the wording of this measure (e.g., whether the incarcerated person or the staff member instigated the incident), correctional staff benefit from a power dynamic in jails that provides options for abstaining from violence against those in their charge unless absolutely necessary. The wording of the measure suggests the staff member intends harm to the individual, providing what we believe to be the best measure available for our purposes given the significant lack of research in this area.

To measure mental illness diagnoses, respondents were asked whether they had ever been diagnosed by a mental health professional such as a psychiatrist or a psychologist with any of the following conditions: (1) manic depression, bipolar disorder, or mania, (2) a depressive disorder, (3) schizophrenia or another psychotic disorder, (4) post-traumatic stress disorder or PTSD, (5) an anxiety disorder such as panic disorder, and (6) personality disorder such as antisocial or borderline. We added these six items together and created a categorical variable to classify the number of mental illness diagnoses as zero, one, and two or more. The “two or more” classification, therefore, indicates the presence of multiple, co-occurring mental illnesses.³

We measured respondent race using a categorical item provided by the NIS-3 alternative survey which asks, “Which of these describes your race?” Responses include (1) White, (2) Black or African American, (3) Hispanic, (4) American Indian or Alaskan Native, (5) Asian, and (6) Native Hawaiian or Pacific Islander. To preserve cases for analysis, we collapsed the last three categories to create an “Other race” category.

We included several controls to account for potential confounding between our main independent and dependent variables. First, we included a binary indicator of whether the respondent was involved in a violent encounter with another incarcerated person over the past 12 months using the question, “Have you been in a fight, assault, or incident in which another inmate tried to harm you?” This is critical to account for any interactions that may otherwise lead the individual to engage in violence with correctional staff. We also included the total admission time for the current incarceration, measured in years by the NIS-3, as well as whether the individual is currently incarcerated for a violent offense. Past research suggests that prior victimization experiences increase the risk of later violence involvement in correctional settings (Blitz et al., 2008; Ellison et al., 2018; Meade et al., 2021; Wolff et al., 2009). We therefore included separate measures for whether the respondent was ever the victim of a physical assault or a sexual assault (0 = no; 1 = yes).

We also accounted for perceptions of the respondent’s facility since these factors may influence the risk of violence in jails (Fox et al., 2012; Rufino et al., 2012; Wolff et al., 2009). We included a measure of perceptions of gang activity in the jail that asks, “How much gang activity has there been at this facility?” and responses included: none, a little, some, and a lot. We recoded this into a binary measure to include the combined responses none/a little (0) or some/a lot (1). No measure of one’s own gang affiliation is available in the NIS-3 Jails data. We created a measure of staff relations to measure respondent perceptions of jail staff which combines binary disagree (0) or agree (1) responses to the prompt, “Please indicate whether you agree or disagree with each of the following statements. Staff at this facility. . .” for the following items: (1) are generally fair, (2) do their best to make this facility safe and secure, (3) try to meet the needs of inmates, (4) break up fights quickly, (5) use physical force only when necessary, (6) let inmates know what is expected of them, (7) generally treat inmates with respect, (8) follow facility rules when handling inmate complaints and grievances, and (9) often write up inmates who don’t deserve it (reverse-coded). These nine items were summed into an index with higher values indicating more positive perceptions of staff relations (Cronbach’s alpha = .873).

We accounted for facility crowding with an index comprised of the following three questions: “How crowded is it in your housing unit?” “How crowded is it outside of the housing unit—for example, in the dining hall, classrooms, gym, or workout area?” and “How much privacy do you have in your housing unit?” Responses for the first two items included: not at all crowded, slightly crowded, pretty crowded, and very crowded. Responses to the privacy question included: none, a little, some, and a lot. We reverse-coded the privacy question so that higher values indicate less privacy. We summed the responses from all three questions such that greater values equate to more facility crowding (Cronbach’s alpha = .705). We include this measure given the expectation that facility crowding likely corresponds to situational safety circumstances that can generate a greater risk for violence and assault at the hands of correctional staff (McNeeley, 2021).

Finally, we included measures of whether the respondent is married (0 = no; 1 = yes) and has a high school degree (0 = no; 1 = yes). We included a binary indicator of self-identified gender (0 = male; 1 = female) and a categorical measure of age (18–24, 25–34, 35–49, and 50+) provided by the NIS-3 to account for demographic differences.

Analytic Strategy

Following a summary of descriptive statistics, we illustrate the prevalence of correctional staff-involved violence by racial group and number of mental illness diagnoses. We report bivariate results for our main variables of interest using Pearson χ² tests of independence and then turn to the multivariate analysis to analyze the relationship between race and mental health and the risk for staff-involved violence. We employ Firth’s logistic regression given the binary nature of the dependent variable and present all results using odds ratios to aid the interpretation of risk (Pampel, 2000). We use the specific estimation proposed by Firth (1993) and developed by Coveney (2021) in Stata given the rare event nature of the dependent variable. The Firth logistic model utilizes a penalized maximum likelihood estimation to reduce bias introduced by rare event variables and resultant standard errors.⁴ The first multivariate model contains the two main independent variables and the second model adds in all control measures. The third and final model presents the results of an interaction analysis between each of the racial categories and the measure of mental health diagnoses while accounting for all controls.

We implement a chained multiple imputation strategy (N = 20 imputations) using the mi impute chained command in Stata to account for missing data (about 18% missing in total). We did not find any substantive differences in the results between the final imputed models and those that used listwise deletion (results available upon request). We conduct all analyses in Stata 17. We did not detect any issues of multicollinearity in our models and provide a full correlation matrix in Supplemental Appendix A1.

Results

Table 1 summarizes descriptive statistics for all variables in the analysis. Approximately 5% of all respondents in our final sample indicate having been involved in a violent encounter with correctional staff. About 38% of the sample self-identified as White while about 34% are Black or African American, 18% are Hispanic, and 10% indicate another racial group. Roughly 35% of all respondents indicate having at least one mental illness diagnosis and about 24% of the sample have multiple diagnoses. Notably, a significant proportion of respondents have experienced physical (64%) or sexual assault (11%). About 20% of the sample is incarcerated for a violent offense and roughly 15% have been involved in a violent altercation with another jailed person. Most respondents are under the age of 34, are unmarried, and are male. About half of the sample indicates having obtained at least a high school degree.

Table 1.

Descriptive Statistics (N = 3,936).

	Mean	SD	Range
Correctional staff violence	0.052	0.222	0–1
Violent altercation with another inmate	0.147	0.353	0–1
Admission time (years)	0.348	0.554	0.002–9.993
Admission for violent offense	0.203	0.403	0–1
Perception of gang activity	0.340	0.474	0–1
Past physical assault	0.640	0.480	0–1
Past sexual assault	0.109	0.312	0–1
Facility crowding	8.118	2.480	3–12
Staff relations	4.279	2.750	0–9
Married	0.179	0.384	0–1
Female	0.135	0.342	0–1
High school education	0.487	0.500	0–1
Manic depression/bipolar/mania	0.195	0.396	0–1
Depressive disorder	0.241	0.428	0–1
Schizophrenia/other psychotic disorder	0.064	0.244	0–1
Post-traumatic stress disorder	0.113	0.317	0–1
Anxiety disorder	0.152	0.359	0–1
Personality disorder	0.095	0.294	0–1
Total # mental illness diagnoses			0–2
Zero	0.647
One	0.116
Two+	0.237
Race			0–4
White	0.379
Black	0.335
Hispanic	0.183
Other	0.103
Age			0–4
18–24	0.365
25–34	0.313
35–49	0.265
50+	0.057

Figure 1 summarizes the prevalence of staff-involved violence by racial group. Approximately 3% of White respondents indicate having been assaulted by a correctional staff member while about 4% in the “Other” race category have been assaulted. In stark contrast, about 7.5% of Black individuals and 7% of Hispanic individuals report having been involved in a correctional staff assault in jail. Figure 2 shows the prevalence of staff assault by the number of mental illness diagnoses indicated by respondents. About 4% of those without a mental illness diagnosis report violence involvement compared to about 7.3% of those with one diagnosis and 6.8% of persons with multiple diagnoses. In the bivariate analysis, we found that number of mental illness diagnoses, χ² (2, N = 3,936) = 13.3, p < .001, and racial group membership, χ² (3, N = 3,936) = 35.8, p < .001 are significantly associated with correctional staff assault. These bivariate results suggest that incarcerated people of color and those experiencing at least one diagnosed mental illness are at heightened risk for violence at the hands of a correctional staff member.

Figure 1.

Racial group and correctional staff violence.

Figure 2.

Mental health diagnoses and correctional staff violence.

Table 2 illustrates the first two models for the multivariate results. In Model 1, both Black and Hispanic respondents are significantly more likely than their White counterparts to report involvement in a violent incident with a staff member. In fact, Black respondents are about 3.4 times more likely to be assaulted by correctional staff while Hispanic respondents are about 3.2 times more likely to be assaulted. Mental illness diagnoses are also significantly predictive of staff-involved violence such that having at least one diagnosis increases the risk of victimization by about 2.2 times while having multiple diagnoses similarly heightens risk by about 2.2 times compared to those with no mental illness diagnosis. The second model includes all control measures. Although the magnitude of the associations is somewhat attenuated, Black and Hispanic respondents are still significantly more likely to report staff-involved violence than White respondents. Similarly, having at least one mental illness diagnosis is associated with a 73% increase in risk for staff-involved violence. Those with two or more diagnoses have a similarly heightened risk (OR = 1.77). Additionally, certain controls are associated with an increased risk of staff-involved violence including an altercation with another jailed person (OR = 3.10), prior physical assault victimization (OR = 1.74), and facility crowding (OR = 1.10). On the other hand, perceptions of better staff relations are associated with a decreased risk of staff assault (OR = 0.78).

Table 2.

Firth’s Logistic Regression Models for Correctional Staff Violence (N = 3,936).

	Model 1		Model 2
	OR	SE	OR	SE
Racial group (ref = White)
Black	3.397***	0.666	2.424***	0.508
Hispanic	3.175***	0.701	2.457***	0.571
Other race	1.395	0.423	1.024	0.324
Mental illness diagnoses (ref = none)
One	2.224***	0.475	1.734**	0.407
Two+	2.203***	0.405	1.771***	0.358
Violent altercation with another inmate			3.100***	0.517
Admission time (years)			1.217	0.146
Violent offense			1.205	0.215
Gang activity			1.395	0.240
Prior physical assault victimization			1.739**	0.358
Prior sexual assault victimization			1.056	0.316
Facility staff relations			0.779***	0.031
Facility crowding			1.099**	0.041
Married			0.892	0.207
Female			0.497	0.190
HS education			1.095	0.175
Age (ref = 18–24)
25–34			0.732	0.190
35–49			0.518**	0.125
50+			0.652	0.304
Constant	0.019***	0.004	0.013***	0.006
Pseudo R²	.06		.26

***

p < .001. **p < .01. *p < .05.

In a supplementary analysis, we substituted an alternate indicator of mental illness to examine how serious mental illness (SMI) corresponds to correctional staff violence. We used the validated six-item K6 scale, designed to discern cases of SMI from non-cases (Kessler et al., 2003). Items in the scale include the following: “During the past 30 days, about how often did you feel. . . (a) nervous?, (b) hopeless?, (c) restless or fidgety?, (d) so depressed that nothing could cheer you up?, (e) that everything was an effort?, and (f) worthless?” Available responses range from none of the time (0) to all of the time (5). All responses were summed together to generate the full measure, ranging from 0 to 24 in our sample. We then used coding provided by the NIS-3, which categorized scores into three groups: no mental illness (0–7), anxiety-mood disorder (8–12), and SMI (13–24). Replicating Model 1 using this measure, we found that having an anxiety-mood disorder significantly increases the risk of correctional staff assault (OR = 1.76, p ≤ .001) compared to having no mental illness. Similarly, SMI substantially increases the risk of correctional staff victimization (OR = 3.06, p < .001). In the fully controlled Model 2 replication, anxiety-mood disorders (OR = 1.60, p = .017) and SMI (OR = 2.232, p < .001) were both significantly associated with increased risk for staff violence. The results, therefore, align closely with the main models using number of mental illness diagnoses, though SMI appears to be a particularly strong risk factor for staff-involved assault.

The third and final model introduces an interaction term between the number of mental illness diagnoses and our categorical indicator of the race (see Table 3).⁵ Contrary to our expectations in H3, we did not find any evidence of a significant interaction between the respondent’s race and an indicator of mental illness diagnoses. Once the interaction is included, there are no statistically significant independent associations between number of diagnoses and staff-involved violence. However, we find an independent effect for race remains such that Black and Hispanic individuals are at higher risk than their White counterparts in jails for staff-involved violence. This suggests that race does not condition the influence of mental illness diagnoses on our outcome but rather operates more saliently as an independent risk factor. We replicated these interaction models using the alternate K6 measure of mental illness and again found no significant interactions between mental illness and race that predict correctional staff violence.

Table 3.

Mental Illness and Race Interactions Predicting Staff-Involved Violence (N = 3,936).

	OR	SE
Racial group (ref = White)
Black	1.800*	0.549
Hispanic	1.996*	0.669
Other race	1.090	0.516
Mental illness diagnoses (ref = none)
One	1.558	0.705
Two+	1.152	0.446
Interactions
Black × One diagnosis	1.458	0.833
Hispanic × One diagnosis	0.864	0.570
Other race × One diagnosis	0.620	0.655
Black × Two+ diagnoses	1.941	0.931
Hispanic × Two+ diagnoses	1.806	0.966
Other race × Two+ diagnoses	1.092	0.764
Constant	0.016***	0.008
Pseudo R²	.26

Note. Model adjusts for all control variables depicted in Table 1.

***

p < .001. **p < .01. *p < .05.

Discussion

We set out to examine how mental illness and race among those incarcerated in jails correspond to the risk of correctional staff-involved violence using nationally representative data. The results produced two key findings. First, in support of H1 and H2, race and mental illness are each independently associated with heightened risk for experiencing staff violence among people incarcerated in jails. Specifically, Black and Hispanic individuals are at much greater risk for staff-involved violence than their White counterparts. Having one or two plus mental illness diagnoses was associated with a greater risk of violence in our fully controlled model. Second, contrary to the expectations of H3, we found that an individual’s race does not condition the relationship between mental illness diagnoses and staff-involved violence. After including this interaction in our model, the independent association between racial groups (Hispanic, Black) with staff-involved violence remained.

Taken together, we assert that both mental illness and race are important for understanding violence committed by correctional staff against jailed people. Although staff violence appears to be relatively rare in jails (about 5% reported staff-involved violence in our sample), the transiency and high rate of turnover in jails may create conditions that lead to greater violence for those suffering from mental illness. Correctional staff unfamiliar with the needs and behaviors of many of those in their charge, including those suffering from mental illness and co-occurring disorders, may perceive certain incarcerated people to be weak or antagonistic. We argue that this dynamic also increases the risk of violent victimization among persons of color because staff may perceive people of color as more violent or threaten than their White counterparts due to both implicit and explicit racial biases (DeLuca et al., 2022; Spector, 2001).

Our main findings corroborate prior research showing that mental illness is a substantial risk factor for violent victimization both in general populations and within correctional settings (Blitz et al., 2008; Grosholz & Semenza, 2021; Latalova et al., 2014; Nowotny et al., 2014). Having multiple diagnoses may render the individual more vulnerable to physical victimization at the hands of a staff member because they are perceived as weaker or more antagonistic than others, even those with a single diagnosis. These individuals may be required to take medications that alter their behavior or act in ways that are interpreted as threatening, leading to greater disciplinary action or altercations with guards that result in the use of force. The supplementary finding that SMI particularly heightens the risk for correctional staff assault underscores that those with the greatest mental health needs are also in the most danger of violent victimization. Similarly, the risk of staff-involved violence is concentrated among incarcerated persons of color, contributing further to significant racial disparities in violence exposure identified in prior literature throughout carceral settings (Warnken & Lauritsen, 2019) and the general population (Berg, 2014; Semenza, Testa, & Jackson, 2022; Zimmerman & Messner, 2013).

The finding that race does not condition the influence of mental illness diagnoses coheres with another study on the subject conducted in a maximum security prison in Minnesota (McNeeley, 2021). Although Black and Hispanic individuals are at greater risk for staff-involved violence in general, individuals with mental illness diagnoses appear to be at similarly heightened risk regardless of race. Since the majority of the sample is White and did not report a mental illness diagnosis, our analysis may not have been able to discern statistically significant interaction effects between race and mental illness due to relatively small numbers of Black or Hispanic respondents also reporting mental illness diagnoses and experiences of correctional staff violence. Theoretically, although a focal concerns perspective would suggest that Black and Hispanic respondents with a mental illness could be in greater danger of staff victimization (Ulmer & Johnson, 2004), these two factors may not augment one another. In other words, correctional staff may still use heuristic short hands to make decisions that lead to a greater risk of violence for people of color or those with mental illness because each is perceived as more antagonistic or dangerous separately. But when it comes to the use of violence, correctional staff may not expect greater danger or antagonism specifically for Black or Hispanic individuals who also have a mental illness. One factor or the other appears to significantly increase the risk of violence but the two do not operate together to increase the risk further.

The results reinforce existing calls for enhanced training of correctional staff (Smoyer et al., 2019; Wooldredge & Steiner, 2016). Correctional staff is entrusted to maintain order and safety within correctional facilities, which includes the important step of modeling prosocial behaviors (Dowden & Andrews, 2004). Assaultive behavior by staff threatens the legitimacy of correctional facilities and contributes to institutional violence (Novisky et al., 2022). Training protocols should ensure staff is trained not only during onboarding but also over time on (a) recognizing and responding to the signs and symptoms of mental illness; and (b) recognizing and addressing racial biases among staff, including implicit biases. Further, McNeeley and Donley (2021) suggest that the crisis intervention team (CIT) model can prove useful in correctional settings. The CIT model may be especially effective in helping correctional staff to make better mental health referrals for those displaying symptoms, although more evaluation is needed to determine the model’s effectiveness for reducing staff-involved violence related to racial bias.

It is also important to ensure jailed persons have practical avenues for reporting correctional staff misconduct. Such avenues would ideally include a system of external reporting and unannounced external audits to limit perceptions of coercion and retaliation among potential reporters. Importantly, disclosure during incarceration may further increase the risk of staff violence so any system must enable individuals to report incidents discreetly, and without risk of retaliation. Prioritization of data gathering that is regularly updated, accessible, and available for public analysis is essential in bolstering these efforts. Exit interviews or surveys are one option for gathering these important data. As we have emphasized throughout this paper, there is a dearth of data on the issue of correctional staff misconduct, especially jail staff misconduct. We used data from 2011 to 2012 in this study because they were the only nationally available data to address the issue of correctional staff violence. Without targeted efforts to increase data gathering and transparency, the problem of correctional staff violence will continue to remain hidden.

Finally, it is notable that key control measures related to perceptions of the respondent’s facility correspond to the differing risk of staff-involved violence such as the perceived quality of staff relations and facility crowding. The finding that better staff relations decrease correctional staff-involved violence suggests that jails should work to improve staff interactions not only to enhance the overall environment of the facility but also to reduce incidents of violence. The measure of staff relations used here includes relatively simple items regarding meeting jailed persons’ needs, following facility protocols for jailed person complaints, and generally treating incarcerated persons with respect, indicating that small changes in the relational dynamics between jailed persons and staff can have potentially outsized benefits. Similarly, facility crowding is found to increase the risk of staff-involved violence, suggesting that jail administrators must make efforts to reduce crowding. Jail overcrowding is a persistent problem in the U.S. (Ruddell & Mays, 2007), yet our results suggest that reducing crowding can have the additional benefit of limiting the incidence of staff-involved violence in addition to improving the general conditions of the facility. There are a number of steps that can be taken to reduce overcrowding in jails that do not entail constructing more facilities to handle overflow including decriminalization of certain offenses such as private marijuana use and public drunkenness, limiting police capacity to indirectly set bail for defendants through filing multiple charges, the establishment of uniform sentence ranges tied to local jail capacity, and emergency release mechanisms (Krajik & Gettinger, 1982; Pontell & Welsh, 1994). In general, efforts to reduce overcrowding by limiting jail populations can both limit the burden of the criminal justice system in general and potentially reduce the risk of violent victimization for those that are incarcerated in jails.

The contributions of this study come with certain limitations. First, the cross-sectional nature of our data precludes us from making causal inferences. Longitudinal data among those in jail are especially difficult to collect given the transient nature of jailed populations, but these data are needed to confirm the causal ordering of the main variables examined here. Second, while the majority of correctional staff consist of security staff or COs, our measure of correctional staff assault does not allow us to rule out the possibility that some of the staff assault reported in the sample was committed by non-custody staff (e.g., medical providers, vendors, vocational and programming staff). Therefore, it should not be assumed that all staff-perpetrated violence in the sample was committed by CO’s or that other types of staff were not engaged in such conduct. In recent research by Novisky et al. (2022) on prison staff misconduct, for example, respondents in the sample cite examples of misconduct not only perpetrated by COs, but also by medical providers and senior correctional management. It will be important for future researchers to collect more detailed measures of correctional staff assault than are available in the current dataset to expand upon the findings we have presented here.

Third, although we took care to include a series of control variables to account for confounding between our main variables of interest, our models may still be subject to missing variable bias. For instance, there was no measure of general misconduct in jails in the NIS-3 alternative survey to assess victim-offender overlap in our models. However, we do control for this to an extent by including a measure of the incarcerated person’s involvement in violence with other jailed persons. Additionally, we focused here on individual-level correlates of correctional staff-involved violence and could not examine facility-level variables using these data (though we do include individual-level perceptions of facility conditions related to crowding, staffing, and gang violence).

Further studies examining staff-involved violence should consider additional risk factors for violence in jails. For instance, although it was beyond the scope of the present study, sexual orientation is a significant risk factor for violent victimization and should be considered in future research on correctional staff violence (Edwards, 2018). Similarly, researchers should examine how CO violence differs based on membership to other ethnic or racial groups, such as American Indian or Alaska Native populations. Although these groups could not be adequately quantified in the present study due to small numbers of respondents, there is potential for disparities in the risk of victimization by correctional staff among these groups compared to their White, Black, and Hispanic counterparts (Ulmer & Bradley, 2019). Continued assessment of how different intersections (e.g., sexual orientation and race, gender, and mental health) influence victimization risk will bolster this area of research.

Finally, it is important to bear in mind that the data used in this study were gathered from people during active periods of incarceration using self-report surveys. Given the lack of privacy in jails and the chance that some respondents could have been concerned about retaliation, it is possible that the prevalence of staff-involved violence was reported conservatively. The respondents likely self-selected into the survey to some degree and caution of interpretation is warranted since those that may actually be the most victimized may have been too frightened to respond. On the other hand, some respondents may simply choose to fill out the survey to break routine and may not answer all questions truthfully. Relatedly, the measure of mental illness diagnoses used here relies on self-reported rather than clinical diagnoses, which may temper the reliability and validity of the construct (Eaton et al., 2000). Future studies should consider the use of clinical measures wherever possible. It is worth considering how results may have varied had data been gathered post-release when respondents were no longer under correctional control or near staff. We encourage future data collection efforts on staff misconduct at other time points, including post-release.

Conclusion

Correctional staff are tasked with watching over those in their charge. Like police officers, we expect that the vast majority of these criminal legal actors treat incarcerated persons with appropriate respect and dignity. However, our study finds that correctional staff-involved violence against people in jails does occur and that the risk of victimization is not equally distributed. Being a person of color and suffering from mental illness are two separate risk factors that render a person in jail more likely to be victimized by correctional staff. This underscores the need both for improved training of correctional staff to help recognize and respond to signs of mental illness and to address issues of racial bias. Those incarcerated in jails must also have the necessary means to report correctional staff violence without the fear of retribution or retaliation. These are critical mechanisms of accountability that can help protect those in jail against potential abuse by the very people charged with their safety.

Supplemental Material

sj-docx-1-jiv-10.1177_08862605221113023 – Supplemental material for Mental Illness and Racial Disparities in Correctional Staff-Involved Violence: An Analysis of Jails in the United States

Supplemental material, sj-docx-1-jiv-10.1177_08862605221113023 for Mental Illness and Racial Disparities in Correctional Staff-Involved Violence: An Analysis of Jails in the United States by Daniel C. Semenza, Jessica M. Grosholz, Deena A. Isom and Meghan A. Novisky in Journal of Interpersonal Violence

Footnotes

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.

Notes

ORCID iD

Daniel C. Semenza

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Daniel C. Semenza, PhD is an assistant professor in the Department of Sociology, Anthropology, and Criminal Justice at Rutgers University—Camden and Director of Interpersonal Violence at the New Jersey Gun Violence Research Center. His research examines the causes and health-related consequences of firearm violence as well as the dynamic links between health, criminal justice contact, and crime.

Jessica M. Grosholz, PhD is an associate professor of criminology at the Sarasota-Manatee campus at the University of South Florida. Her research focuses on prisoner reentry and recidivism, health and crime, human trafficking, and qualitative field research.

Deena A. Isom, PhD is an associate professor in the Department of Criminology and Criminal Justice and the African American Studies Program at the University of South Carolina. Her research aims to understand the causes and consequences of disparities in criminal behaviors and contact with the justice system.

Meghan A. Novisky, PhD is an associate professor of criminology at Cleveland State University. Her research investigates the consequences of carceral contact on health, factors related to conditions of confinement, and the latent consequences of criminal justice policy. Her most recent work has been published in Criminology, Justice Quarterly, and the Journal of Interpersonal Violence.

References

Avent Harris

J. R.

Crumb

Crowe

Garland McKinney

. (2020). African Americans’ perceptions of mental illness and preferences for treatment. Journal of Counselor Practice, 11(1), 1–33.

Berg

M. T.

(2014). Accounting for racial disparities in the nature of violent victimization. Journal of Quantitative Criminology, 30(4), 629–650. https://doi.org/10.1007/s10940-014-9217-6

Blitz

C. L.

Wolff

Shi

(2008). Physical victimization in prison: The role of mental illness. International Journal of Law and Psychiatry, 31, 385–393. https://doi.org/10.1016/j.ijlp.2008.08.005

Bolger

P. C.

(2015). Just following orders: A meta-analysis of the correlates of American police officer use of force decisions. American Journal of Criminal Justice, 40(3), 466–492. https://doi.org/10.1007/s12103-014-9278-y

Bronson

Berzofsky

(2017). Indicators of mental health problems reported by prisoners and jail inmates, 2011–12. U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics.

Bureau of Labor Statistics. (2021). Occupational employment and wages, May 2021: 33-3012 correctional officers and jailers. http://www.bls.gov/oes/current/oes333012.htm

Centers for Disease Control and Prevention, US Department of Health and Human Services. (2016). Health, United States, 2015: With special feature on racial and ethnic health disparities (DHHS Publication No. 2016-1232). https://www.cdc.gov/nchs/data/hus/hus15.pdf

Chang

Martin

Marrapodi

(2020, August 16). Summer of racial reckoning. National Public Radio. Retrieved June 16, 2022, from https://www.npr.org/2020/08/16/902179773/summer-of-racial-reckoning-the-match-lit

Cohen

L. E.

Felson

(1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588–608. https://doi.org/10.2307/2094589

10.

Coveney

(2021). FIRTHLOGIT: Stata module to calculate bias reduction in logistic regression. Available at: https://econpapers.repec.org/software/bocbocode/S456948.htm

11.

DeLisi

(2019). Antagonism and crime. In Miller

J. D.

Lyman

D. R.

(Eds.), The handbook of antagonism: Conceptualizations, assessment, consequences, and treatment of the low end of agreeableness (pp. 297–309). Elsevier Academic Press.

12.

DeLuca

J. S.

Hwang

Stepinski

Yanos

P. T.

(2022). Understanding explanatory mechanisms for racial and ethnic differences in mental health stigma: The role of vertical individualism and right-wing authoritarianism. Journal of Mental Health, 31(1), 39–49. https://doi.org/10.1080/09638237.2020.1836556

13.

Department of Justice. (2020). Investigation of Alabama’s state prisons for men. United States Department of Justice, Civil Rights Division. Retrieved June 17, 2022, from https://www.justice.gov/crt/case-document/file/1297031/download?utm_medium=email&utm_source=govdelivery

14.

Dowden

Andrews

D. A.

(2004). The importance of staff practice in delivering effective correctional treatment: A meta-analytic review of core correctional practice. International Journal of Offender Therapy and Comparative Criminology, 48(2), 203–214. https://doi.org/10.1177/0306624X03257765

15.

Eaton

W. W.

Neufeld

Chen

L. S.

Cai

(2000). A comparison of self-report and clinical diagnostic interviews for depression: Diagnostic interview schedule and schedules for clinical assessment in neuropsychiatry in the Baltimore epidemiologic catchment area follow-up. Archives of General Psychiatry, 57(3), 217–222.

16.

Edwards

K. M.

(2018). Incidence and outcomes of dating violence victimization among high school youth: The role of gender and sexual orientation. Journal of Interpersonal Violence, 33(9), 1472–1490.

17.

Edwards

Lee

Esposito

(2019). Risk of being killed by police use of force in the United States by age, race–ethnicity, and sex. Proceedings of the National Academy of Sciences, 116(34), 16793–16798. https://doi.org/10.1073/pnas.1821204116

18.

Ellison

J. M.

(2017). An opportunity model of victimization risk for jail inmates and correctional officers (Publication Number: 10617177) [Doctoral dissertation, University of Nebraska Omaha]. ProQuest Dissertations Publishing.

19.

Ellison

J. M.

Steiner

Wright

E. M.

(2018). Examining the sources of violent victimization among jail inmates. Criminal Justice and Behavior, 45(11), 1723–1741. https://doi.org/10.1177/0093854818788590

20.

Engel

Sobol

Worden

(2000). Further exploration of the demeanor hypothesis: The interaction effects of suspects’ characteristics and demeanor on police behavior. Justice Quarterly, 17(2), 235–258. https://doi.org/10.1080/07418820000096311

21.

Finkelhor

Asdigian

N. L.

(1996). Risk facts for youth victimization: Beyond a lifestyles/routine activities theory approach. Violence and Victims, 11, 3–19.

22.

Firth

(1993). Bias reduction of maximum likelihood estimates. Biometrika, 80(1), 27–38.

23.

Fox

K. A.

Lane

Akers

R. L.

(2012). Understanding gang membership and crime victimization among jail inmates: Testing the effects of self-control. Crime & Delinquency, 59(5), 764–787. https://doi.org/10.1177/0011128710392003

24.

Garofalo

(1986). Lifestyles and victimization: An update. In Fattah

E. A.

(Ed.), From crime policy to victim policy: Reorienting the justice system (pp. 135–155). Palgrave Macmillan.

25.

Greif Green

McLaughlin

K. A.

Fillbrunn

Fukuda

Jackson

J. S.

Kessler

R. C.

Sadikova

Sampson

N. A.

Vilsaint

Williams

D. R.

Cruz-Gonzalez

Alegría

. (2020). Barriers to mental health service use and predictors of treatment drop out: Racial/ethnic variation in a population-based study. Administration and Policy in Mental Health and Mental Health Services Research, 47, 606–616. https://doi.org/10.1007/s10488-020-01021-6

26.

Greifinger

R. B.

(2007). Thirty years since Estelle v. Gamble: Looking forward, not wayward. In Greifinger

R. B.

(Ed.), Public health behind bars: From prisons to communities (pp. 1–12). Springer.

27.

Griffin

M. L.

(1999). The influence of organizational climate on detention officers’ readiness to use force in a county jail. Criminal Justice Review, 24(1), 1–26. https://doi.org/10.1177/073401689902400102

28.

Grosholz

J. M.

Semenza

D. C.

(2021). Health conditions and victimization among incarcerated individuals in U.S. jails. Journal of Criminal Justice, 74, 101797. https://doi.org/10.1016/j.jcrimjus.2021.101797

29.

Gushue

G. V.

(2004). Race, color-blind racial attitudes, and judgements about mental health: A shifting standards perspective. Journal of Counseling Psychology, 51(4), 398–407. https://doi.org/10.1037/0022-0167.51.4.398

30.

Hindelang

M. J.

Gottfredson

M. R.

Garofalo

(1978). Victims of personal crime: An empirical foundation for a theory of personal victimization. Ballinger Publishing.

31.

Hogan

N. L.

Lambert

E. G.

Hepburn

J. R.

Burton

V. S.

Cullen

F. T.

(2005). Is there a difference? Exploring male and female correctional officers’ definition of and response to conflict situations. Women & Criminal Justice, 15, 143–165. https://doi.org/10.1300/J012v15n03_07

32.

Hollis

M. E.

Jennings

W. G.

(2018). Racial disparities in police use-of-force: A state-of-the-art review. Policing: An International Journal, 41(2), 178–193. https://doi.org/10.1108/PIJPSM-09-2017-0112

33.

Irwin

(1985). The jail: Managing the underclass in American society. University of California Press.

34.

Johnson

B. D.

(2005). Contextual disparities in guidelines departures: Courtroom social contexts, guidelines compliance, and extralegal disparities in criminal sentencing. Criminology, 43(3), 761–796. https://doi.org/10.1111/j.0011-1348.2005.00023.x

35.

Kessler

R. C.

Barker

P. R.

Colpe

L. J.

Epstein

J. F.

Gfroerer

J. C.

Hiripi

Howes

M. J.

Normand

S. L.

Manderscheid

R. W.

Walters

E. E.

Zaslavsky

A. M.

(2003). Screening for serious mental illness in the general population. Archives of General Psychiatry, 60(2), 184–189.

36.

Krajik

Gettinger

S. H.

(1982). Overcrowded time: Why prisons are so crowded and what can be done. Edna McConnell Clark Foundation.

37.

Kramer

Remster

(2018). Stop, frisk, and assault? Racial disparities in police use of force during investigatory stops. Law & Society Review, 52(4), 960–993. https://doi.org/10.1111/lasr.12366

38.

Latalova

Kamaradova

Prasko

(2014). Violent victimization of adult patients with severe mental illness: A systematic review. Neuropsychiatric Disease and Treatment, 10, 1925. https://doi.org/10.2147/NDT.S68321

39.

Lee

L. H.

Goodkind

Shook

J. J.

(2017). Racial/ethnic disparities in prior mental health service use among incarcerated adolescents. Children and Youth Services Review, 78, 23–31. https://doi.org/10.1016/j.childyouth.2017.04.019

40.

Maura

Weisman de Mamani

(2017). Mental health disparities, treatment engagement, and attrition among racial/ethnic minorities with severe mental illness: A review. Journal of Clinical Psychology in Medical Settings, 24, 187–210. https://doi.org/10.1007/s10880-017-9510-2

41.

McNeeley

(2021). Racial disparities in use of force against incarcerated people. Corrections, 1–22. https://doi.org/10.1080/23774657.2021.1900757

42.

McNeeley

Donley

(2021). Crisis intervention team training in a correctional setting: Examining compliance, mental health referrals, and use of force. Criminal Justice and Behavior, 48(2), 195–214. https://doi.org/10.1177/0093854820959394

43.

Meade

Wasileski

Hunter

(2021). The effects of victimization prior to prison on victimization, misconduct, and sanction severity during incarceration. Crime & Delinquency, 67(12), 1856–1878. https://doi.org/10.1177/0011128720977440

44.

Morabito

M. S.

Socia

Wik

Fisher

W. H.

(2017). The nature and extent of police use of force in encounters with people with behavioral health disorders. International Journal of Law and Psychiatry, 50(1), 31–37. https://doi.org/10.1016/j.ijlp.2016.10.001

45.

National Center on Addiction and Substance Abuse at Columbia University. (2010). Behind bars II: Substance abuse and America’s prison population. CASA Columbia.

46.

Nettelbeck

Wilson

(2002). Personal vulnerability to victimization of people with mental retardation. Trauma, Violence, & Abuse, 3, 289–306.

47.

Novisky

M. A.

Narvey

Piquero

(2022). The keepers: Returning citizens’ experiences with prison staff misconduct. Criminal Justice & Behavior, 49(7), 1010–1030 . https://doi.org/10.1177/00938548211028895

48.

Nowotny

K. M.

Belknap

Lynch

DeHart

(2014). Risk profile and treatment needs of women in jail with co-occurring serious mental illness and substance use disorders. Women & Health, 54(8), 781–795. https://doi.org/10.1080/03630242.2014.932892

49.

Pampel

F. C.

(2000). Logistic regression: A primer. Sage.

50.

Paoline

E. A.

III Gau

J. M.

Terrill

(2018). Race and the police use of force encounter in the United States. The British Journal of Criminology, 58(1), 54–74. https://doi.org/10.1093/bjc/azw089

51.

Pare

P.-P.

Logan

M. W.

(2011). Risk of minor and serious violent victimization in prison: The impact of inmates’ mental disorders, physical disabilities, and physical size. Society and Mental Health, 1(2), 106–123. https://doi.org/10.1177/2156869311416828

52.

Pontell

H. N.

Welsh

W. N.

(1994). Incarceration as a deviant form of social control: Jail overcrowding in California. Crime & Delinquency, 40(1), 18–36.

53.

Rastogi

Massey-Hastings

Wieling

(2012). Barriers to seeking mental health services in the Latino/a community: A qualitative analysis. Journal of Systemic Therapies, 31(4), 1–17. https://doi.org/10.1521/jsyt.2012.31.4.1

54.

Rossler

M. T.

Terrill

(2017). Mental illness, police use of force, and citizen injury. Police Quarterly, 20(2), 189–212. https://doi.org/10.1177/1098611116681480

55.

Ruddell

Mays

G. L.

(2007). Rural jails: Problematic inmates, overcrowded cells, and cash-strapped counties. Journal of Criminal Justice, 35(3), 251–260.

56.

Rufino

K. A.

Fox

K. A.

Kercher

G. A.

(2012). Gang membership and crime victimization among prison inmates. American Journal of Criminal Justice, 37, 321–337. https://doi.org/10.1007/s12103-011-9134-2

57.

Semenza

Novisky

(2020). Mental health and services in prisons and jails. In Birch

Sicard

L. A.

(Eds.), Prisons and community corrections (pp. 189–202). Routledge.

58.

Semenza

D. C.

Testa

Jackson

D. B.

(2022). Intersectional differences in serious violent victimization trajectories across the life course. Preventive Medicine Reports, 26, 101732.

59.

Shjarback

J. A.

Nix

(2020). Considering violence against police by citizen race/ethnicity to contextualize representation in officer-involved shootings. Journal of Criminal Justice, 66, 101653. https://doi.org/10.1016/j.jcrimjus.2019.101653

60.

Smoyer

A. B.

Madera

J. E.

Blankenship

K. M.

(2019). Older adults’ lived experiences of incarceration. Journal of Offender Rehabilitation, 58(3), 220–239. https://doi.org/10.1080/10509674.2019.1582574

61.

Spector

(2001). Is there a racial bias in clinicians’ perceptions of the dangerousness of psychiatric patients? A review of the literature. Journal of Mental Health, 10(1), 5–15. https://doi.org/10.1080/09638230020023570

62.

Steffensmeier

Ulmer

Kramer

(1998). The interaction of race, genera, and age in criminal sentencing: The punishment cost of being young, Black, and male. Criminology, 36(4), 763–797. https://doi.org/10.1111/j.1745-9125.1998.tb01265.x

63.

Stohr

M. K.

Hemmens

Collins

P. A.

Iannacchione

Hudson

Johnson

(2012). Assessing the organizational culture in a jail setting. The Prison Journal, 92(3), 358–387. https://doi.org/10.1177/0032885512448614

64.

The Marshall Project. (2021). Correctional officer brutality: A curated collection of links. The Marshall Project. Retrieved June 29, 2021, from https://www.themarshallproject.org/records/1949-correctional-officer-brutality

65.

Tully

(2021, June 7). Women’s prison plagued by sexual violence will close, governor says. The New York Times. Retrieved June 29, 2021, from https://www.nytimes.com/2021/06/07/nyregion/womens-prison-edna-mahan-closing-nj.html

66.

Ulmer

J. T.

Bradley

M. S.

(2019). Criminal justice in Indian country: A theoretical and empirical agenda. Annual Review of Criminology, 2(1), 337–357.

67.

Ulmer

J. T.

Johnson

(2004). Sentencing in context: A multilevel analysis. Criminology, 42(1), 137–177. https://doi.org/10.1111/j.1745-9125.2004.tb00516.x

68.

Venters

(2019). Life and death in Rikers Island. Johns Hopkins University Press.

69.

Warnken

Lauritsen

(2019). Who experiences violent victimization and who accesses services? Findings from the National Crime Victimization Survey for expanding our reach. https://ncvc.dspacedirect.org/bitstream/item/1270/CVR%20Article_Who%20Experiences%20Violent%20Victimization%20and%20Who%20Accesses%20Services.pdf?%20sequence=1

70.

Western

(2018). Homeward: Life in the year after prison. Russell Sage Foundation.

71.

Wilcox

Hunt

S. A.

Land

K. C.

(2003). Criminal circumstance: A dynamic multi-contextual criminal opportunity theory. Walter de Gruyter.

72.

Wolff

Blitz

C. L.

Shi

Siegel

Bachman

(2007). Physical violence inside prisons: Rates of victimization. Criminal Justice and Behavior, 34, 588–599. https://doi.org/10.1177/0093854806296830

73.

Wolff

Shi

Siegel

(2009). Understanding physical victimization inside prisons: Factors that predict risk. Justice Quarterly, 26(3), 445–475. https://doi.org/10.1080/07418820802427858

74.

Wooldredge

(2020). Prison culture, management, and in-prison violence. Annual Review of Criminology, 3, 165–188. https://doi.org/10.1146/annurev-criminol-011419-041359

75.

Wooldredge

Steiner

(2012). Race group differences in prison victimization experiences. Journal of Criminal Justice, 40, 358–369. https://doi.org/10.1016/j.jcrimjus.2012.06.011

76.

Wooldredge

Steiner

(2014). A bi-level framework for understanding prisoner victimization. Journal of Quantitative Criminology, 30(1), 141–162. https://doi.org/10.1007/s10940-013-9197-y

77.

Wooldredge

Steiner

(2016). The exercise of power in prison organizations and implications for legitimacy. The Journal of Criminal Law & Criminology, 106(1), 125–165. https://scholarlycommons.law.northwestern.edu/cgi/viewcontent.cgi?article=7582&context=jclc

78.

Zeng

Minton

T. D.

(2021). Jail inmates in 2019. U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics.

79.

Zimmerman

G. M.

Messner

S. F.

(2013). Individual, family background, and contextual explanations of racial and ethnic disparities in youths’ exposure to violence. American Journal of Public Health, 103(3), 435–442. https://doi.org/10.2105/AJPH.2012.300931

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB