Operational Definitions of Poly-Victimization: A Scoping Review

Abstract

Throughout the last two decades, research on poly-victimization (PV) has evolved from examinations of a core set of past-year victimization types in youth samples to investigations of a broad range of victimization types experienced during variable time intervals in diverse samples of varying ages. As the concept of PV expands, greater clarity regarding the definition and measurement of PV is needed to advance understanding of its risk and protective factors as well as its associated outcomes. This scoping review aimed to (a) identify approaches used to operationally define and measure PV across studies and (b) synthesize empirical findings concerning risk factors and outcomes associated with PV. A systematic search of peer-reviewed research published before 2022 across 12 databases yielded 98 studies that met inclusion criteria. Study characteristics including the research design, sample type, victimization timeframe, operational definition(s) of PV, measurement of PV, analytic methods, and key findings were synthesized across studies. Findings indicated that the majority of reviewed studies were cross-sectional investigations that utilized categorical measures of lifetime PV in samples of youth. Results also demonstrated that PV is robustly associated with a broad range of predictors, including mental health symptoms and diagnoses as well as family- and community-level factors. PV is also associated with numerous adverse outcomes including depression, anxiety, suicidality, substance use, and delinquency across diverse study types and populations. Future research that examines the conditional effects of PV is needed to identify subgroups of individuals at higher risk of adverse outcomes following PV and modifiable targets for interventions.

Keywords

poly-victimization victimization measurement definition

Empirical studies concerning the impact of violence and victimization on psychological health and psychosocial functioning are consistent in showing that exposure to violence and victimization is associated with a wide range of negative outcomes, including depression (e.g., Andrews et al., 2015), anxiety (e.g., Charak et al., 2019), posttraumatic stress (e.g., Ayer et al., 2019), substance use (e.g., Davis et al., 2020), and suicidality (e.g., Ford et al., 2018). However, the majority of research on victimization has focused on outcomes linked to a single victimization type, such as sexual assault, bullying, or intimate partner violence (IPV). Given that individuals who report exposure to violence and victimization are likely to experience more than one type of victimization during their lifetime (Finkelhor, Ormrod, & Turner, 2009) as well as exposure across multiple settings (i.e., home, school, and in the broader community; Butcher et al., 2016; Finkelhor, Ormrod, et al., 2009), studies that examine single categories of victimization likely underestimate the full burden of individuals’ victimization histories. Furthermore, studies that examine the effects of victimization based on assessments of single victimization types may overestimate the impact of particular types on survivor outcomes and bias findings concerning individual-difference factors associated with elevated risk of adverse outcomes. Conclusions about the effects of victimization drawn from studies that fail to assess individuals’ exposure to a broad spectrum of victimization types may also misguide the development of interventions designed to prevent or reduce the negative consequences of victimization.

To address these concerns, scholars across multiple disciplines have begun to investigate the cumulative impact of exposure to multiple types of victimization experiences. Finkelhor et al. introduced the term “poly-victimization (PV)” in 2005 based on research showing that youth exposed to multiple types of victimization exhibit more severe negative outcomes compared to youth who experience one victimization type (Finkelhor, Ormrod, et al., 2005) as well as youth who experience repeated exposure to the same type of victimization (Finkelhor et al., 2007b). Results consistent with these findings have since emerged in research on clinical and nonclinical samples of poly-victims of varying ages (i.e., children, adolescents, adults) and backgrounds (e.g., incarcerated women, Radatz & Wright, 2017; foster care youth, Schneiderman et al., 2013) as well as across diverse fields of study, including criminology, psychology, public health, social work, and sociology.

As research on PV has advanced, how PV has been operationally defined and measured has likewise expanded. In Finkelhor, Ormrod et al.’s (2005) seminal study, poly-victims were defined as youth who experienced four or more different types of lifetime or past-year victimizations that occurred during separate incidents. An additional distinction was made between low PV (four to six types) and high PV (six or more types) subgroups among poly-victims. In subsequent research, alternative a priori approaches to operationally defining PV have been used, including exposure to specific numbers of victimization types (e.g., four or more types; Cuevas et al., 2009) and percentile thresholds (e.g., participants in the top 10% of the sample; Finkelhor, Ormrod, & Turner 2009). Various methods of weighting victimization types for severity and chronicity have also been used to account for differences in incident severity and repeated exposure to a single victimization type among poly-victims (e.g., Finkelhor et al., 2007a; Finkelhor, Ormrod, & Turner, 2009).

In contrast to these a priori variable-centered approaches to operationally defining PV, several recent studies have used empirically driven person-centered methods, including latent class analysis (LCA) and latent profile analysis (LPA). These methods identify homogeneous classes or subgroups of participants within a sample that have similar patterns of exposure to victimization types. Studies that use person-centered methods of defining PV indicate the particular types of victimization that tend to co-occur, which may advance understanding of the phenomenology of PV and facilitate efforts to identify individuals in greatest need of prevention and intervention efforts.

Given the substantial variability in how PV has been defined in research across a broad range of disciplines, a review of the existing literature is needed. Accordingly, we conducted a scoping review of peer-reviewed studies on PV to summarize how PV has been operationally defined and measured throughout behavioral science and medical research and to synthesize empirical findings concerning risk factors and outcomes associated with PV. Current gaps in the literature were also identified.

Method

Search Strategy

A systematic search of peer-reviewed research journals was conducted by a university social science librarian. The following databases were searched for articles published between January 1, 2000 and October 22, 2021: ERIC on ProQuest, National Criminal Justice Reference Service, Social Services Abstracts on ProQuest, Web of Science, Sociological Abstracts on ProQuest, PsycInfo on Ebscohost, EbscoHost Academic Search Complete, Criminal Justice Abstracts on Ebscohost, PubMed, and PILOTS: Published International Literature On Traumatic Stress on ProQuest. Titles, abstracts, and subjects were searched using the following terms: (measure* OR define* OR definition* OR defining OR assess* OR diagnos*) AND (polyvictimi* OR “poly-victimization” OR “poly-victimisation” OR “poly-victimised” OR “poly-victim”). Duplicates (n = 918) were first omitted from the initial 1,420 identified articles. Authors then reviewed the abstracts of the remaining 502 articles and eliminated 377 that did not meet the following inclusion criteria: (a) empirical research; (b) quantitative study; (c) published in a peer-reviewed journal; (d) the intent to examine PV as an independent variable (IV), dependent variable (DV), or mediator was stated in the article title or abstract; and (e) examined a U.S. sample. The full texts of the remaining 125 articles were reviewed, and 27 were excluded due to the following reasons: (a) duplicate articles (n = 2); (b) non-U.S. sample (n = 4); (c) PV was not an IV, DV, or mediator (n = 20); and (d) not available in English (n = 1). Figure 1 summarizes the search process.

Figure 1.

PRISMA flow diagram.

For the remaining 98 unique articles, the following categories of information were extracted and are summarized in Table 1: author(s) and publication year; youth or adult sample; victimization timeframe; operational definition(s) of PV and variable type; PV measure; PV variable type (IV, DV, or mediator); and analysis type. To ensure consistency between authors who screened articles for inclusion eligibility and extracted key study characteristics, 10 articles were randomly selected and reviewed by two co-authors each (five authors total). The agreement of inclusion eligibility decisions between authors was 100%. Agreement of key study characteristics reported in Table 1 was 96% (67/70). Differences were discussed among all reviewing authors until reconciled. The remaining 88 articles were reviewed by four authors.

Table 1.

Key Characteristics of Reviewed Studies

S. N.	Authors (Date)	Sample	Victimization Timeframe	Operational Definition(s) of PV [Variable Type]	PV Measure	PV Is IV or DV	Analysis Type
1	Adams et al. (2016)	Youth	Lifetime	PV subgroups identified based on multi-informant reports of number of potentially traumatic events during single vs. multiple developmental epochs [categorical]	UCLA PTSD-RI	IV	Regression, LCA
2	Alexander et al. (2018)	Adults	Childhood	Self-reported number of victimization types [continuous]	JVQ (adult retrospective)	IV	Regression
3	Alexander et al. (2021)	Youth	Lifetime	Low PV (average of 8.85 victimizations) and high PV (average of 15.86 victimizations) classes identified based on self-reported exposure to different victimization types and trauma-related symptoms [categorical]	JVQ	IV	Latent profile analysis
4	Alexander et al. (2021)	Adults	Childhood	Self-reported number of victimization types [continuous]	JVQ (adult retrospective)	IV	Regression, mediation
5	Andrews et al. (2015)	Youth	Lifetime	Self-reported number of traumatic experiences [continuous]	Unstandardized	IV	SEM
6	Andrews et al. (2019)	Youth	Lifetime, past year	Self-reported number of victimization types [continuous]	NSA-R	IV	Path models
7	Ataiants et al. (2020)	Adults	Lifetime	Self-reported number of victimization types [continuous]; low: ≤8 victimization types, high: 9–16 victimization types [categorical]	TLEQ (adapted), UVPSP (adapted)	IV	Regression
8	Ayer et al. (2019)	Youth	Lifetime	PV class identified based on self-reported exposure to victimization types [categorical]	JVQ (modified)	IV	LCA, growth curve analysis
9	Baams (2018)	Youth	Lifetime	PV class identified based on self-reported exposure to different types of childhood adversity [categorical]	ACEs	DV	LCA, regression
10	Blain et al. (2012)	Adults	Childhood	Self-reported exposure to types of childhood trauma [continuous]	CTQ	IV	Regression
11	Bridges-Curry and Newton (2022)	Adults	Lifetime	PV class identified based on self-reported exposure to victimization types [categorical]	LTVH	IV	LCA, direct, and indirect effects
12	Burnett et al. (2016)	Adults	Older adulthood	Two or more types of concurrent elder abuse [categorical]	Unstandardized	IV	Regression, survival analysis
13	Burns et al. (2016)	Adults	Lifetime	Victimization profiles identified based on self-reported lifetime exposure to interpersonal traumas [categorical]	Unstandardized	IV	Regression, LCA
14	Butcher et al. (2016)	Youth	Past year	PV classes identified based on self-reported exposure to different types of violence as either a witness or a victim across three contexts (home, school, and neighborhood) [categorical]	REVS	IV and DV	Regression, chi-square, LCA
15	Butcher et al. (2020)	Youth	Lifetime	Poly-location group identified based on self-reported exposure to victimization types [categorical]	REVS	DV	LTA
16	Carbonaro (2019)	Youth	Past year, or prior 2 years	(a) Number of self-reported victimization types [continuous]; (b) PV subgroups identified based on self-reported number of victimization types [categorical]; and (c) self-reported frequency of parental abuses and frequency of victimizations outside the home (continuous)	Add Health	IV	LCA
17	Charak et al. (2019)	Youth	Lifetime	PV class identified based on self-reported exposure to different types of adversity [categorical]	UCLA PTSD-R-5, unstandardized	IV	LCA MANCOVA
18	Christie and Matthews (2019)	Adults	Lifetime	Self-reported frequency of violence [continuous], two or more self-reported exposures to violence [categorical]	CTQ, CEQ	IV	Regression, mediation
19	Connell et al. (2018)	Youth	Lifetime	PV classes identified based on self- and caregiver-reported number of different potentially traumatic events [categorical]	Unstandardized	IV and DV	LCA
20	Cudmore et al. (2017)	Youth	Past year	Self-reported exposure to victimization types [continuous]	JVQ (modified)	IV	Regression
21	Cuevas et al. (2009)	Youth	Past year	Number of self- or caregiver-reported victimization types [continuous], exposure to four or more victimization types [categorical]	JVQ (modified)	DV	ANCOVA
22	Cuevas et al. (2020)	Youth	Past year	Self-reported number of different types of victimizations [continuous]	JVQ (modified), CTS2 short form	DV	Regression
23	Daigle et al. (2021)	Adults	Past year	Two or more types of self-reported IPV [categorical]	ACHA-NCHA III	IV and DV	Regression
24	Davis et al. (2018)	Youth	Past year	PV classes identified based on self- and caregiver-reported victimization types and parental hostility on one or more occasions [categorical]	QPRI and EVI	IV	LCA, latent growth models
25	Davis et al. (2019)	Youth	Lifetime	PV classes identified based on self-reported childhood abuse types and abuse characteristics (chronicity, closeness of perpetrator, fear for life/injury, negative reactions to disclosure) [categorical]	GAIN	IV	LCA
26	Davis et al. (2020)	Youth	Past year	Self- and caregiver-reported exposure to two or more victimization types [categorical]	QPRI maternal and paternal hostility, EVI	IV	Growth mixture modeling, survival analysis
27	De Vries and Farrell (2018)	Adults	Lifetime and within labor trafficking stages	Two or more types of violence or threat related to trafficking [categorical]	Unstandardized	DV	Regression
28	DeHart and Moran (2015)	Youth	Lifetime	Self-reported number of exposures to victimization types [continuous]	LHC, JVQ	IV	Survival analysis, regression
29	Dierkhising et al. (2019)	Youth	Three developmental stages	PV classes identified based on multi-informant reported exposure to traumatic childhood event types during three developmental stages [categorical]	UCLA PTSD-RI-5	IV	LCA, LTA multinomial regression
30	Douglas et al. (2021)	Youth	Lifetime	Self-reported number of traumatic childhood events [continuous]	UCLA PTSD-RI-5	Mediator	Mediation
31	Elliott et al. (2009)	Adults	Childhood	Self-reported number of childhood victimization types [continuous]	JVQ-R2	IV	Regression
32	Farrell and Zimmerman (2017)	Youth	Past year	Self-reported exposure to two or more types of violence [categorical]	Add Health	IV	Regression
33	Farrell and Zimmerman (2018)	Youth	Past year	Self-reported exposure to two or more types of violence [categorical]	Add Health	IV	Regression
34	Finkelhor, Ormrod et al. (2005)	Youth	Past year	Five definitions based on self- or caregiver-report: (a) number of victimization types that occurred during separate incidents (Separate Incident Version) [continuous], (b) total victimization types (Screener Sum Version) [continuous], (c) total items endorsed from subset of Screener Sum Version (Reduced Item Version) [continuous], (d) four or more victimization types that occurred during different incidents [categorical], and (e) low PV: 4–6 types, high PV: 7 or more types [categorical]	JVQ (modified)	IV	Regression
35	Finkelhor et al. (2007a)	Youth	Past year, past year controlling for lifetime	Past year: Self- or caregiver-reported exposure to four or more victimization types that occurred during separate incidents [categorical], Lifetime: Number of victimization types experienced prior to the past year [continuous]	JVQ (modified)	IV	Regression
36	Finkelhor et al. (2007b)	Youth	Past year	Three PV definitions based on self- or caregiver-report: (a) number of victimization types [continuous], (b) three or more victimization types, and (c) low PV: 4 to 6 victimization types, high PV: 7 or more victimization types [categorical]	JVQ (modified)	IV	Regression, ANOVA
37	Finkelhor, Ormrod, and Turner (2009)	Youth	Past year and lifetime	Top 10% of each age group based on total self- or caregiver-reported victimization types +4 for any maltreatment and +3 for any sexual assault [categorical (cutoff)]	JVQ (modified)	IV	Regression
38	Finkelhor, Ormrod, Turner, and Holt (2009)	Youth	Past year and lifetime	Past year: self- or caregiver-reported exposure to 5 or more victimization types [categorical]; lifetime: ages 3–6 = 9+ types, ages 7–10 = 10+ types; ages 11–14 = 12+ types; ages 15–18 = 15+ types [categorical]	JVQ (modified)	DV	Regression, cluster analysis
39	Finkelhor et al. (2011)	Youth	Past year	Top 10% of each age group based on self- or caregiver-reported victimization types [categorical (cutoff)]	JVQ (modified)	IV	ANOVA, regression
40	Fogleman et al. (2021)	Youth	Past 30 days	Self-reported exposure to one in-person victimization plus one cyber-victimization in the past 30 days [categorical]	PBFS-AR	IV	Regression
41	Ford et al. (2010)	Youth	Lifetime	PV class identified based on self-reported exposure to multiple victimization types [categorical]	Unstandardized	IV	Regression
42	Ford et al. (2011)	Youth	Lifetime	PV subgroup identified based on two or more types of victimization using self-report and other-report data, clinical interviews, medical records, and Child Protective Service records [categorical]	Unstandardized	IV	Regression, cluster analysis
43	Ford et al. (2013)	Youth	Lifetime	Poly-victim class identified based on self-reported exposure to potentially traumatic events or adversity [categorical]	TESI, UCLA PTSD-RI, MAYSI-2	IV	ANOVA, MANOVA, LCA
44	Ford et al. (2018)	Youth	Lifetime	PV class identified based on the number of potentially traumatic childhood events [categorical]	UCLA PTSD-RI	IV	SEM, path analysis, LCA
45	Forke et al. (2021)	Adults	Lifetime	Self-reported exposure to more than one type of violence [categorical]	CTS2 (modified)	DV	Regression
46	French et al. (2014)	Youth and YAs	Lifetime	PV class identified based on multiple types of self-reported sexual victimization and tactics [categorical]	SCI	IV and DV	LCA, t-tests
47	Garnett and Brion-Meisels (2017)	Youth	Current school year, 30 days	Self-reported number of school victimization types [continuous]	MSSSCS	IV	Regression
48	Grasso et al. (2016)	Youth	Past year	PV class identified based on multi-informant reports of witnessing IPV and experiencing maltreatment [categorical]	CTS2, CTSPC	IV	LCA, distal outcomes analysis
49	Grasso et al. (2019)	Youth	Lifetime	Self-reported exposure to six or more trauma domains [categorical]	STRESS	DV	ROC curves
50	Hamby et al. (2018)	Adults	Lifetime	Digital PV: Self-reported number of online or cellphone-based adverse experiences [continuous], In-person PV: Self-reported number of different types of interpersonal victimization [continuous]	DVS, JVQ (key domains form)	IV	Regression
51	Harrelson et al. (2017)	Youth	Lifetime	Self-reported number of victimization types [continuous]	JVQ-R2	IV	Regression
52	Hasselle et al. (2017)	Adults	Childhood	Self-reported number of types of violence [continuous]	JVQ-R2	IV	Regression
53	Hickman et al. (2013)	Youth	Lifetime	Caregiver-reported number of victimization types [continuous], 2+ types of caregiver-reported victimization [categorical]	JVQ (Caregiver Report Version)	IV	Regression
54	Horn et al. (2018)	Youth	Lifetime	Number of different maltreatment types documented in child welfare case records [continuous]	MCS	IV	Regression
55	Johns et al. (2020)	Youth	Past year	Self-reported exposure to two or more forms of victimization [categorical]	Unstandardized	IV	Regression
56	Kerig and Modrowski (2018)	Youth	Lifetime	Self-reported number of victimization experiences [continuous]	UCLA PTSD-RI	IV	SEM
57	King (2022)	Adults	Childhood	Self-reported number of high frequency (>1 SD) child maltreatment types [continuous]	Unstandardized	IV	Regression
58	King et al. (2015)	Youth	Lifetime	More than one type of medically diagnosed child maltreatment [categorical]	ICD-9-CM	DV	Regression
59	Kretschmar et al. (2017)	Youth	Past year	PV classes identified based on self-reported exposure to victimization types [categorical]	JVQ short form (modified)	IV and DV	LCA, regression, chi-square
60	Landers et al. (2021)	Adults	Childhood	Self-reported number of child abuse types [continuous]	Unstandardized	DV	Regression
61	Listwan et al. (2014)	Adults	Last year of incarceration	Self-reported number of different types of victimization witnessed or experienced in prison [continuous]	Unstandardized	DV	Regression
62	López et al. (2017)	Youth	Lifetime	Number of self-reported exposure to traumatic event types [continuous]	NSA-R	IV and DV	Regression
63	McNair et al. (2019)	Youth	Lifetime	Exposure to two or more forms of victimization [categorical]	UCLA PTSD-RI (modified)	IV	Regression
64	Mendez et al. (2019)	Youth	Lifetime	Multi-informant reported number of interpersonal violence victimization types [continuous]	Unstandardized	IV	SEM
65	Messinger et al. (2022)	Adults	Lifetime	Self-reported exposure to victimization types [continuous]	Unstandardized	DV	Regression
66	Miller-Graff et al. (2019)	Adults	Childhood	Self-reported number of violence types experienced in childhood [continuous]	JVQ-R2	IV	Path analysis
67	Modrowski et al. (2021)	Youth	Lifetime	“PV dominant” class identified based on self-reported exposure to adversity/victimization events [categorical]	PTSD-RI, CTQ	IV	LCA
68	Radatz and Wright (2017)	Adults	Lifetime	(a) Self-reported exposure to more than the sample average number (7) of victimization types [categorical] (cutoff), (b) low PV: 8–9 victimization types, and (c) high PV: top one-third of the sample (10–14 victimization types) [categorical]	Unstandardized	IV	t-Tests, chi-squares, regression
69	Reidy et al. (2017)	Youth	Lifetime	Self-reported number of violence types [continuous]	YRBS	IV	Regression
70	Richmond et al. (2009)	Adults	Lifetime	Self-reported exposure to different types of victimization [continuous]	JVQ-R2	IV	Regression
71	Scheer et al. (2021)	Youth	Past year, lifetime	PV class identified based on self-reported types of physical and sexual victimization [categorical]	YRBS	IV and DV	LCA, regression
72	Schneiderman et al. (2013)	Youth	Lifetime	Other-reported exposure to two or more child maltreatment types [categorical]	Unstandardized	IV	Regression
73	Schwab-Reese et al. (2021)	Youth and adults	Varied	Self-reported exposure to two or more types of victimization [categorical]	Add Health measure	DV	Regression
74	Semenza (2019)	Youth	Past year	Self-reported number of online and offline peer victimization types [continuous]; High PV: four or more online and offline peer victimization types [categorical]	Unstandardized	IV	Regression
75	Sherman et al. (2020)	Adults	Lifetime	Self-reported number of exposures to violence [continuous]	PVI	IV	Regression
76	Sherman et al. (2021)	Adults	Lifetime	Self-reported exposure to different forms of direct interpersonal violence [continuous]	THQ	IV	Regression
77	Snyder et al. (2021)	Adults	Past year	Self-reported exposure to two or more victimization types [categorical]	Unstandardized	DV	Regression
78	Sterzing et al. (2017)	Youth and YAs	Past year	Self-reported exposure to 10 or more victimizations [categorical (cutoff)]	Abbreviated JVQ, SBS	DV	Regression
79	Sterzing, Fisher, et al. (2019)	Youth	Past year	Self-reported exposure to 10 or more victimizations [categorical (cutoff)]	Abbreviated JVQ, SBS	DV	LCA, path analysis
80	Sterzing, Gartner, et al. (2019)	Youth	Lifetime	(a) 15 or more self-reported victimization types [categorial (cutoff)] and (b) low PV: 15–19 types, high PV: 20+ types [categorical (cutoff)]	Abbreviated JVQ, SBS	DV	Bivariate logit analyses
81	Stover et al. (2019)	Youth	Lifetime	Multi-informant report of four or more victimization types [categorical]	UCLA PTSD-RI	IV and DV	Regression
82	Turner et al. (2010)	Youth	Lifetime	Based on self- and caregiver-report: (a) number of different victimization types [continuous] and (b) top 10% of the sample (11 or more different types) [categorical (cutoff)]	Enhanced JVQ	IV	Regression
83	Turner et al. (2012)	Youth	Past year	Self-reported exposure to seven or more victimization types [categorical]	Enhanced JVQ	IV	Regression
84	Turner, Finkelhor et al. (2013)	Youth	Past year	Self- or caregiver-reported number of victimization types [continuous]	Enhanced JVQ	IV and DV	Regression
85	Turner, Shattuck et al. (2013)	Youth	Past year	Self-reported number of victimization types [continuous]	JVQ	Mediator	SEM
86	Turner et al. (2016)	Youth	Past year	PV class identified based on self-reported number of victimization types experienced in different settings (home, school, community) [categorical]	JVQ (modified)	IV	Chi-square, ANOVA, LCA
87	Turner et al. (2017)	Youth	Past year	Four PV groups were identified based on the number of self- or caregiver-reported types: (a) stable low PV: below sample mean in Waves 1 and 2; (b) declining PV: above sample mean in Wave 1, below sample mean in Wave 2; (c) increasing PV: below sample mean in Wave 1, above sample mean in Wave 2; and (d) stable-high PV: above sample mean at both waves [categorical]	JVQ (modified)	IV	SEM
88	Turner, Mitchell et al. (2019)	Youth	Lifetime	Self-reported exposure to five or more victimization types [categorical]	JVQ (modified)	IV	Regression
89	Turner, Vanderminden et al. (2019)	Youth	Lifetime	Self- and caregiver-reported number of victimization types [continuous]	JVQ	IV	Regression
90	Turner et al. (2021)	Youth	Lifetime	Self-reported exposure to 11 or more victimization types [categorical]	JVQ (modified)	IV	Chi-square, t-tests, regression
91	Updegrove and Muftic (2019)	Youth and YAs	Childhood	Self-reported exposure to two or more types of victimization [categorical]	ACES	IV	ANOVA, chi-square
92	Williams et al. (2020)	Adult	Past year	Self-reported experience of two or more categories of elder mistreatment [categorical]	Unstandardized	DV	Regression
93	Williams et al. (2021)	Adults	Lifetime	Interaction between two forms of self-reported interpersonal trauma [categorical]	CTS2, SES-SFV, ACES	IV	Regression
94	Willie and Kershaw (2018)	Youth and YAs	Lifetime	PV classes identified based on self-reported exposure to different violence indicators [categorical]	Unstandardized	IV	LCA, path analysis, ANOVA
95	Wojciechowski (2020)	Youth	Lifetime	(a) Self-reported number of different exposures to direct victimization [continuous] and (b) self-reported number of different exposures to witnessed violence [continuous]	Unstandardized	IV	Regression
96	Wolf and Prabhu (2018)	Adults	Childhood	Cumulative childhood PV: Self-reported number of childhood traumatic events [continuous]; PV/witnessing trauma: Self-reported witness to one or more types of trauma [categorical]; PV/experiencing trauma: Self-reported child sexual abuse plus one or more types of trauma [categorical]	BBTS	IV	Regression
97	Wong et al. (2016)	Youth and adults	Lifetime prior to homelessness	Self-reported number of traumatic and adverse experiences prior to homelessness [continuous]	Unstandardized	IV	Regression
98	Wright and Wachs (2021)	Youth	Lifetime	Self-reported number of different forms of cyber PV [continuous], self-reported number of offline victimization experiences [continuous]	Unstandardized, JVQ	IV	Regression

Note. S.N. = study number. PV = poly-victimization; UCLA PTSD-RI = University of California at Los Angeles Posttraumatic Stress Disorder Reaction Index; LCA = latent class analysis; JVQ = Juvenile Victimization Questionnaire; SEM = structural equation modeling; NSA-R = National Survey of Adolescents-Replication; TLEQ = Traumatic Life Events Questionnaire; UVPSP = Universal Violence Prevention Screening Protocol; ACEs = Adverse Childhood Experiences Study Questionnaire; CTQ = Childhood Trauma Questionnaire; LTVH = Lifetime Trauma and Victimization History Questionnaire; REVS = Recent Exposure to Violence Scale; LTA = latent transition analysis; Add Health = National Longitudinal Study of Adolescent to Adult Health; UCLA PTSD-RI-5 = Trauma History Profile from the University of California Los Angeles Posttraumatic Stress Disorder Reaction Index for the DSM-5; MANOVA = multivariate analysis of variance; MANCOVA = multivariate analysis of covariance; CEQ = Community Experiences Questionnaire; ANCOVA = analysis of covariance; CTS2 = Revised Conflict Tactics Scale; ACHA-NCHA = American College Health Association’s National College Health Assessment; QPRI = Quality of Parental Relationships Inventory; EVI = Exposure to Violence Inventory; GAIN = Global Appraisal of Individual Needs; QPRI = Quality of Parental Relationships Inventory; LHC = Life History Calendar; ANOVA = analysis of variance; PBFS-AR = The Problem Behavior Frequency Scale—Adolescent Report; TESI = Trauma Events Screening Instrument; MAYSI-2 = Massachusetts Youth Screening Instrument-2; YAs = young adults; SCI = Sexual Coercion Inventory; MSSSCS = Maryland Safe and Supportive School Climate Survey; STRESS = Structured Trauma-Related Experiences and Symptoms Screener; ROC = Receiver Operating Characteristic Curves; DVS = Digital Victimization Scale; MCS = Maltreatment Classification System; ICD-9-CM = International Classification of Diseases; YRBS = Youth Risk Behavior Surveillance System; PVI = Poly-Victimization Inventory; SBS = Swearer Bullying Survey; SES-SFV = Sexual Experiences Survey–Short Form Victimization; BBTS = Brief Betrayal Trauma Survey; IPV = intimate partner violence; CTSPC = Conflict Tactics Scales Parent-Child version; THQ = Trauma History Questionnaire.

Findings

Research Design of Reviewed Studies

The majority of studies (n = 82) were cross-sectional. The remaining 16 studies were longitudinal (S6, S8, S12, S15, S22, S26, S32, S33, S35, S38, S64, S73, S83, S87, S95, and S98). Several studies analyzed cross-sectional data derived from longitudinal datasets (e.g., S24, S29).

Research Questions

Most studies (n = 69) examined PV as an IV to test its impact on or association with aspects of survivors’ psychosocial functioning, health, or behavior. The majority (n = 64) of these studies examined mental health outcomes, including trauma symptoms (S37, S39, S53, S82, S86, S88, S89, S90, S91, and S96). Other common outcomes included delinquency (S20, S28, S41, S56, S62, S64, and S90) and criminal behavior (S16, S28, S32, S33, and S51). Another set of studies (n = 18) examined PV as a DV in relation to risk factors or correlates, such as demographic characteristics, including race (S60), sexual and gender minority status (S9, S61, S65, S73, S77, S78, S80, and S92), family and environmental factors (S38 and S78), and mental health symptoms or disorders (S21 and S77). In addition, nine studies examined PV as both an independent and a DV (S14, S19, S23, S46, S59, S62, S71, S81, and S84). An analysis of data from the National Survey of Children’s Exposure to Violence (NatSCEV) examined PV as a mediator of the association between community disorder and distress symptoms (S85), and another study examined PV as a mediator of the association between race/ethnicity and posttraumatic stress symptoms (S30). Protective factors for PV, such as social support (S61) and religiosity (S78), were also examined.

Samples

The majority of studies (n = 65) examined youth under age 18. Six studies examined youth and adults ages 18 and older. The remaining 27 examined adults only. Nationally representative samples were examined in 27 studies (S5, S6, S13, S16, S21, S32, S33, S34, S35, S36, S37, S38, S39, S41, S55, S62, S71, S73, S82, S83, S84, S85, S86, S87, S89, S90, and S92). Of these, nine analyzed data from the NatSCEV (S39, S82, S83, S84, S85, S86, S87, S89, and S90). Single-gender samples were common, with 12 studies examining females only (S2, S7, S18, S28, S31, S67, S68, S70, S71, S75, S76, and S77) and 6 males only (S3, S4, S10, S13, S51, and S61). Thirteen studies (S14, S17, S26, S28, S43, S44, S49, S56, S61, S63, S67, S68, and S95) examined justice-involved populations. Other studies examined samples from particular sub-populations or ethnicities, including undergraduates (S2, S4, S11, S18, S23, S31, S45, S46, S52, S66, and S77), gender minority populations (S10, S65, S75, S76, S78, and S79), older adults (S12), foster care youth (S54, S60, and S72), and Latinos (S20 and S22).

Respondent Type

Victimization experiences were primarily documented via self-report. In studies examining children, the cutoff age for self-report was typically 10 years old (i.e., studies that analyzed NatSCEV data, studies that administered the Juvenile Victimization Questionnaire [JVQ, Finkelhor, Hamby et al., 2005]), but occasionally a different threshold was selected (e.g., S21). When the sample included only young children, caregivers typically reported youth victimization histories. A few studies combined self- and/or caregiver-report data with administrative (e.g., Child Protective Services records) or clinical data (S1, S18, S28, S42, S64, and S81), whereas other studies exclusively utilized administrative data or clinical data (S12, S25, S54, and S72). Medical diagnostic codes were exclusively used in one study (S58).

Mode

Phone interviews were a common (n = 20) mode of data collection and used in several studies of nationally representative samples, including the Developmental Victimization Survey (S21, S34, S35, S36, S37, and S38), NatSCEV (S39, S82, S83, S86, S87, S84, S85, and S90), the National Survey of Adolescents (S41), the National Survey of Adolescents-Replication (NSA-R) Study (S5, S6, and S62), and the National Elder Mistreatment Study (S92). Surveys (i.e., researcher- or clinician-administered, online, and paper formats, n = 45), in-person interviews (n = 10), computer-assisted interviews (n = 4), or a combination (n = 12) of methodologies were also used. Several studies used data drawn from administrative, clinical, or health records (S1, S12, S25, S27, S54, S58, and S72).

Operational Definitions of PV and Measurement

All reviewed studies broadly defined PV as self- or other-reported exposure to different types of victimization, potentially traumatic events, abuse/maltreatment, trauma, or adversities. Using this definition as a foundation, studies then varied with respect to the following dimensions of PV: victimization types, victimization timeframe, PV variable types (IV, DV, or mediator), co-occurring victimization types, and victimization severity.

Victimization Types

The victimization types examined in each study were typically determined by the measure used to assess victimization exposure. Many studies (n = 10) measured PV using the original (Finkelhor, Hamby, et al., 2005) or revised JVQ (JVQ-R2), which assesses 34 types of victimization categorized into five domains: conventional crime, child maltreatment, peer and sibling victimization, sexual victimization, and witnessing and indirect victimization. Several studies utilized alternative or modified versions of the JVQ (n = 25) including an adult retrospective version (S2 and S4), an adolescent/caregiver version (S53), abbreviated versions (S50, S59, and S78–S80), and an enhanced version (S82, S83, and S84).

Other standardized measures of victimization included the Recent Exposure to Violence Scale (Singer et al., 1999; S14, S15), the Exposure to Violence Inventory (Selner-O’Hagan et al., 1998; S26, S24), the Universal Violence Prevention Screening Protocol (Dutton et al., 1996; S7), the Maltreatment Classification System (Barnett et al., 1993; S54), the Sexual Coercion Inventory (Waldner et al., 1999; S46), the Problem Behavior Frequency Scale (Farrell et al., 2018; S40), and the Conflict Tactics Scale (Straus, 1979; S22, S45, S48, and S93). Trauma-focused victimization measures included the Traumatic Experiences Screening Instrument (Ford et al., 2000; S43), the Trauma History Questionnaire (Hooper et al., 2011; S76), the Childhood Trauma Questionnaire (Bernstein & Fink, 1998; S10, S18, and S67), the Trauma History Profile from the UCLA Posttraumatic Stress Disorder-Reaction Index (UCLA PTSD-RI; Steinberg et al., 2004) or the revised UCLA PTSD-RI-5 (S1, S17, S29, S30, S43, S44, S56, S63, S67, and S81), the Traumatic Life Events Questionnaire (Kubany et al., 2000; S7), the Brief Betrayal Trauma Survey (Goldberg & Freyd, 2006; S96), and the Lifetime Trauma and Victimization History Questionnaire (Widom et al., 2005; S11). Several studies used measures designed to assess general adversity, such as the Adverse Childhood Experiences Study Questionnaire (Felitti et al., 1998; S9, S91, and S93), or a specific type of childhood adversity, such as bullying (S78–S80). A few studies measured PV using medical diagnostic codes (S58) or behavioral health screening instruments, such as the Global Appraisal of Individual Needs (S25), the Massachusetts Youth Screening Instrument (S43), and questions from the Youth Risk Behavior Surveillance System (S71). In addition, many studies assessed individuals’ history of violence or traumatic events using measures that are not specific to victimization, such as the Life History Calendar (Freedman et al., 1988; S28) and the Community Experiences Survey (Schwartz & Proctor, 2000; S18).

Among studies that did not utilize standardized measures to assess victimization (n = 21), most examined a broad range of victimization types. Others focused on specific types of victimization, including childhood maltreatment (S57, S60, and S72), elder abuse (S12), IPV types (S23), violence witnessed or experienced in prison (S61), and online victimization (S98). Two studies used both standardized and unstandardized measures to assess PV (S7 and S17).

Victimization Timeframe

Most studies examined either lifetime PV (n = 53) or past-year PV (n = 27). Ten studies on adults examined childhood PV. A few studies (S6, S37, and S38) measured both lifetime and shorter victimization timeframes and compared their relative impact. Other victimization timeframes included the last 2 years (S22), the last 30 days (S40 and S47), the last 12 months of incarceration (S61), the current school year (S47), and older adulthood (S12).

In studies that define PV as lifetime exposure to different types of victimization, age is a potential confounding variable given that older respondents have had more time to be exposed to victimization than younger respondents. The role of victim age is further complicated by results showing that after young adulthood the likelihood of violent victimization in a given year decreases (see Garofalo, 1979). The potential confounding role of respondent age has been addressed in several ways in research on PV. In particular, age is often used as a control variable in multivariate statistical models. Alternatively, many studies limit samples to specific age groups, such as childhood and adolescence (n = 65) or older adulthood (S12). Other strategies included testing separate statistical models for different age groups (S34, S35, and S36), tracking age of onset (S28 and S38), defining PV as respondents with more than the top 10% of total victimizations within their age group (S37, S39, and S82), and measuring PV at multiple points throughout the lifespan (S98).

PV Variable Types

Most studies (n = 54) analyzed a categorical PV variable. Of those, many used categories selected a priori based on prior research (e.g., S34) or an a priori selected cutoff. For example, in a few studies, the number of victimization types experienced by respondents was assessed and individuals with scores in the top 10% of the sample (or each age group) were categorized as poly-victimized (S37, S39, and S82). Another set of studies categorized individuals who experienced greater or fewer victimization types than a selected threshold into high PV and low PV groups (S3, S7, S34, S36, S68, and S80). Others defined PV (S35, S36, and S68) or high PV (S7) as more than the sample average number of victimization types. In contrast, S39 and S68 categorized participants with scores in the top one-third of the sample as high PV. One study identified stable-low, increasing, decreasing, and stable-high PV subgroups by comparing the number of victimization types endorsed at two time points (S87).

A large minority (n = 33) of studies utilized a continuous PV variable defined as the total number of victimization types experienced. Eleven studies (S7, S16, S18, S21, S34, S35, S36, S53, S74, S82, and S96) analyzed PV as both a continuous and a categorical variable. Among this group, several (S16, S34, S35, S53, and S96) examined differences between models that tested categorical and continuous PV variables.

Finally, approximately one-quarter of the reviewed studies (n = 25) examined one or more PV profiles using LCA (S1, S8, S9, S11, S13, S14, S16, S17, S19, S24, S25, S29, S41, S43, S44, S46, S48, S59, S67, S71, S86, and S94), LPA (S3), cluster analysis (S42), or latent transition analysis (S15) to identify homogeneous classes or subgroups of participants within a sample who had similar patterns of exposure to various types of victimization. S43 identified latent classes based on the number of victimization types and categorized participants into high, moderate, and low levels of victimization. The setting or context of the victimization, such as home, school, community, or poly-location, defined subgroups in several studies (S14, S15, S16, and S86). Other studies defined subgroups according to the developmental timing of the victimizations (S1, S13, and S29).

Co-occurring Victimization Types and Severity Weighting

Although most studies did not specify how the potential co-occurrence of victimization types was accounted for in their definition of PV, a few (S20, S32, S33, S34, S35, S38, and S59) explicitly required different victimization types to occur on separate occasions. For example, S34 and S35 used the Separate Incident Version of the JVQ which includes questions to determine the number of victimizations that occurred at different times and places. Victimization types were weighted for severity prior to the construction of the PV variable in three studies (S35, S37, and S80).

Empirical Findings

Impact of PV

Despite the substantial variability in how PV is operationally defined throughout the literature, results are relatively consistent in showing that PV is a robust predictor of adverse outcomes. The majority of both cross-sectional and longitudinal studies found a linear relationship between PV and greater symptom severity, number of symptoms, or frequency of problematic behavior. Furthermore, this pattern of results was reported for a broad spectrum of psychological health variables, including depression (S5, S11, S17, S19, S31, S34, S40, S44, S48, S57, S63, S70, S75, S96, and S97), anxiety (S11, S17, S31, S34, S40, S44, S48, S50, S57, S70, and S96), substance use/abuse (S1, S7, S17, S24, S25, S26, S28, S31, S32, S33, S43, S44, S63, S64, S67, S68, S76, S93, and S95), PTSD (S1, S3, S5, S6, S8, S11, S13, S17. S19, S30, S43, S48, S50, S63, S66, S67, S75, and S97), suicidality (S1, S2, S17, S31, S43, S44, S55, S67, S68, and S83), and emotion dysregulation (S11, S17, S43, S57, and S70); sexual risk behaviors (S2, S46, S67, and S94); as well as delinquency (S20, S28, S41, S56, S62, S64, and S90) and criminal behavior (S16, S28, S32, S33, and S51). This observed relationship between PV and negative outcomes was also generally consistent across studies that analyzed continuous measures of PV and in studies that used categorical measures in which poly-victims were compared to single-victimized individuals and/or individuals with histories of no victimization. Studies that utilized empirically driven methods of defining PV (e.g., LCA, LPA) also generally found that poly-victimized subgroups within their samples exhibited higher levels of negative outcomes compared to less victimized and/or non-victimized subgroups. In contrast, results from the few studies that examined physical health outcomes associated with PV revealed mixed findings regarding the negative physical health consequences of PV, such as high body mass index (BMI) (S18 and S72) and mortality (S12).

Risk Factors for PV

Despite the wide range of risk factors examined across the reviewed studies, results were relatively consistent in showing that mental health-related predictors, including PTSD (S78 and S81), eating disorders (S77), psychiatric diagnoses broadly defined (S21 and S81), and internalizing and externalizing behaviors (S14 and S81); social predictors, including peer rejection (S78) and membership in sororities and fraternities (S77); family-related predictors, including residential instability (S84), family problems (S38), parental conflict (S84), and family or maternal history of mental illness (S14, S15, and S84); school-related predictors, such as school climate (S78); and indices of community danger (S38) and disorder (S84), are associated with higher levels or greater odds of PV. Only two studies (S61 and S77) found that PV was not significantly associated with the hypothesized risk factors, including higher BMI, learning disabilities, physical disabilities, drug use, gang affiliation, and treatment participation. Of the few studies that examined protective factors, social support (S22, S61, and S92) and religiosity (S78) were found to be negatively associated with PV or associated with lower risk.

Mediators and Moderators of PV

Although relatively few studies (n = 13) examined the processes through which PV leads to negative outcomes, a broad spectrum of potential explanatory mechanisms were tested, including emotion dysregulation (S11, S24, and S79), posttraumatic stress (S64 and S79), insecure caregiver attachment (S51), executive functioning (S54), and social support (S87). All studies reported evidence of mediation except S18 and S54. Contrary to study hypotheses, no evidence was found to support the role of cortisol as a mediator of the association between PV and BMI (S18). Similarly, executive functioning did not mediate the association between PV and externalizing problems in S54.

Four studies (S5, S30, S62, and S85) examined PV as a mediator. Analyses of NSA-R data (S5) indicated that PV mediated differences in mental health symptoms between ethnicity groups, such that mental health disparities between Hispanic and White youth and between non-Hispanic Black youth and White youth were explained by higher levels of PV among the ethnic minority groups. Similarly, in a separate analysis of NSA-R data (S62), PV accounted for ethnic disparities in delinquency, PTSD symptoms, and depression symptoms. Analyses of NatSCEV data indicated that PV mediated the association between community disorder and symptoms of distress (S85). Finally, among bereaved youth, the association between race and elevated posttraumatic stress was mediated by higher PV (S30).

The reviewed studies also included several investigations of potential moderators of the association between PV and psychological health and psychosocial functioning (S5, S20, S54, S55, S62, S69, S75, and S98) or the association between a predictor variable and PV (S73). Information about the conditional effects of PV yielded by moderation studies may help to identify intervention targets as well as subgroups at higher risk of adverse outcomes following victimization. The majority of studies that tested moderators reported results that can be used to advance these goals (S5, S54, S55, S62, S69, and S98). S5 reported that the association between PV and poor mental health was stronger in youth from low-income families compared to youth from moderate-to-high income families. Similarly, PV was more strongly associated with sexual and injury perpetration among boys compared to girls, and among younger compared to older youth in S69. In a nationally representative sample of adolescents (S62), PV was more strongly associated with delinquent behavior in non-Hispanic White compared to Hispanic youth, but no evidence of moderation was found for PTSD symptoms, substance use, or depression symptoms. In S55, the associations between suicide risk and PV varied by sexual minority status, such that sexual minority youth with histories of PV had higher odds of suicide risk behaviors compared to heterosexual youth with histories of PV. Similarly, PV was related to externalizing problems among children with low executive functioning scores, but not among children with high executive functioning scores in S54. Finally, among middle school students (S98), parental social support moderated the associations between cyber PV and academic outcomes (e.g., school readiness, academic performance, absenteeism).

An additional four studies reported nonsignificant results from tests of moderation. In S20, no evidence was found to indicate social support moderated the relation between PV and hostility. Similarly, neither age nor gender moderated the association between PV and sexual or injury victimization in S69. Gender also did not moderate the association between sexual orientation and PV in S73. Finally, in a sample of transgender and gender-diverse adults (S75), the association between PV and depression was not moderated by community connection.

Discussion

Throughout the last two decades, research on PV has expanded from a focus on a core set of past-year victimization types assessed by the JVQ in samples of youth to investigations of a broad range of victimization types experienced during variable time intervals in diverse study populations. As the concept of PV continues to evolve, greater clarity with respect to the definition and measurement of PV is needed to advance understanding of its prevalence, risk and protective factors, associated outcomes, and effective interventions. The goals of this scoping review were to identify the approaches used to operationally define and measure PV in extant research and to summarize empirical findings concerning risk factors and outcomes associated with PV. Our findings indicate that the majority of studies were cross-sectional investigations that utilized categorical measures of lifetime PV in samples of youth. In addition, despite substantial diversity in research designs, study populations, and measurement methods, our results indicate that PV is robustly associated with a broad range of predictor variables, including mental health symptoms and diagnoses as well as family- and community-related factors. PV is also robustly associated with numerous adverse outcomes including depression, anxiety, suicidality, substance use, and delinquency. Although very few studies reported nonsignificant findings for hypothesized risk factors or outcomes, divergent or inconsistent results across studies can likely be attributed to variability in the operation definitions of PV used in prior studies, which ranged from continuous variables indexing the total number of victimization types experienced during a given timeframe, categorical variables that divided samples into groups, and person-centered methods that identify latent groups of poly-victims relative to individuals exposed to no or low levels of victimization.

The reviewed studies also used a diverse array of methods to assess PV, including tailored versions of the JVQ (Finkelhor, Hamby, et al., 2005) and other victimization questionnaires, as well as standardized and unstandardized measures of trauma exposure, adversity, and behavioral health. Given that the victimization types examined in a study are largely determined by the measure used to assess PV, the wide range of measurement methods employed in the reviewed studies limits the ability to compare results across studies. This variability may also pose challenges to clinical decision-making regarding screening and treatment. To advance the field in identifying the optimal method of measuring PV, more research is needed that compares methods used to assess and define PV in diverse samples. In particular, studies that compare measurement methods as well as continuous and categorical definitions of PV may yield critical information regarding the relative explanatory power of different definitions and measures of PV in relation to outcomes that can be used to inform intervention efforts.

Another dimension of PV along which studies varied is the temporal co-occurrence of victimization types. Although most studies do not specify whether the definition of PV required victimization types to occur on separate occasions, some studies identified victimization types (e.g., rape and robbery) that occurred at the same time and place as PV while others did not. In addition, some victimization measures conceptualize victimization types that typically involve a combination of victimization experiences (e.g., sexual assault and physical abuse during an incident of IPV) as a single victimization type, which may underestimate exposure to PV among violence victims.

Methods of accounting for and defining victimization severity also varied across studies. In addition, studies rarely developed criteria for assessing or weighting the severity of victimization types before construction of the PV variable which can be problematic. For example, when severity is exclusively based on the number of victimizations, an individual who experienced multiple types of theft (e.g., on person, in home, from vehicle) could be categorized as having higher levels of PV than an individual exposed to repeated sexual abuse. Multiple experiences of the same type of severe victimization (e.g., sexual abuse) may also be obscured in PV definitions that index the number of victimization types without accounting for event severity and chronicity. Operational definitions that index the number of victimization types, incident severity, and chronicity may better capture individuals’ lived experience of victimization compared to those that assess only the total number of victimization types.

Despite the rapid growth in research on PV throughout the last two decades, our review revealed several gaps in the existing research (Table 2). First, very few investigations employed longitudinal research designs. Additional longitudinal research is warranted given the potential for the impact of PV to change over time and for individuals to experience more victimization types with age. Longitudinal data also have the potential to yield critical insight into factors that promote acute versus long-term consequences of PV as well as greater understanding of the conditional effects of PV when examined using moderation analyses. Second, relatively few studies to date have examined factors that modify the adverse effects of PV on psychological health and well-being. Additional research on potential moderators of the influence of PV on a range of outcomes is needed to identify targets for intervention efforts that seek to prevent future victimization and promote recovery in affected populations. In addition, the field is dominated by studies of risk factors for PV with less attention paid to protective factors. Intervention studies are also scarce. Given the prevalence of PV and the wide range of associated negative consequences, the field would benefit from greater understanding of protective factors and effective interventions that reduce the risk of future victimization, minimize the immediate and long-term negative effects of PV, and promote resilience in poly-survivors. In general, this work may be especially beneficial for minority and oppressed groups who are overrepresented among violence survivors.

Table 2.

Implications of Research on PV.

Design
• The majority of research on PV is cross-sectional. Additional longitudinal research is needed to advance understanding of changes in the impact of PV across the life course.
• Most studies focus on factors associated with heightened risk of PV. More research on protective factors and interventions that reduce the risk of future victimization, minimize the immediate and long-term negative effects of PV, and promote resilience is needed.

Sample and respondent type
• The majority of studies examined youth samples. More research with adults of different ages would inform an understanding of how PV and its outcomes vary across the lifespan.
• Additional research comparing child-reported and caregiver-reported PV is needed to determine the validity and reliability of caregiver-reported PV.
• To identify ecologically valid and modifiable risk and protective factors that can inform prevention and treatment efforts, greater precision and attention to differences in the operational definitions of PV used in research on diverse populations is needed.
• Future studies should describe the population(s) to which their results can be generalized based on the demonstrated variability in the definitions of PV used in research on different developmental epochs, in different macro (e.g., ethnoracial, national) and micro (e.g., communities) cultures, and in different community and clinical populations.
• The consistency of results showing that PV is a stronger correlate of adverse outcomes compared to single or repeated victimization of the same type suggests that research on PV has the potential to inform public health approaches to violence prevention and treatment. This work may be especially beneficial for minority and oppressed groups who are overrepresented among violence survivors.

Measures and operational definitions of PV
• Although studies are generally consistent in demonstrating that PV is a robust correlate of a diverse range of adverse outcomes, differences in the measurement and operational definitions of PV likely contribute to divergent findings regarding the prevalence of PV.
• Standardized definitions of PV are needed to improve estimates of prevalence rates as well as the identification of modifiable risk and protective factors.
• Studies that compare measures of PV are needed to advance understanding of the optimal method of assessing PV.
• Definitions of PV that account for the number of victimization types, number of perpetrators, incident severity, and chronicity may more accurately represent the lived experience of poly-survivors compared to definitions based exclusively on the number of victimization types.

Analysis
• Prospective research is needed to identify antecedent predictors and long-term effects of PV.
• A greater understanding of the conditional effects of PV afforded by moderation studies may help to identify subgroups of individuals at heightened risk of adverse outcomes following PV and modifiable targets for preventive interventions.
• Additional mediation studies are needed to advance understanding of the processes through which PV leads to negative psychological health and impaired psychosocial functioning.
• Person-centered statistical techniques that identify homogeneous subgroups of poly-victims are promising approaches to advancing understanding of the phenomenology of PV.

Note. PV = poly-victimization.

Research on changes in the impact of PV across the lifespan is another critical gap in the literature. The majority of reviewed studies examined youth under age 18, which advances understanding of risk factors and outcomes associated with PV during childhood and adolescence, but not differences in the predictors and consequences of PV across the life course. Furthermore, in studies on youth samples, youths’ PV history is typically reported by parents which may impact accuracy. Additional research comparing child-reported and caregiver-reported PV is needed to determine the validity and reliability of caregiver-reported PV. In addition, similar to victimization research in general, many studies included in our review are characterized by dual validity challenges wherein residential samples are not representative of the general population because they are more victimized by definition, and community samples often include healthier individuals who are better able or motivated to discuss traumatic experiences (Schneider, 1981). Investigations of PV in national and large-scale population-based studies could address these limitations of the existing research.

In addition, few studies used measures of PV that assessed the victim’s relationship to the perpetrator (see S38) or victimization in multiple contexts (see S14) which may index victimization perpetrated by different individuals (e.g., adults, peers). As a result, understanding of how PV outcomes are differentially influenced by the victim’s relationship to the perpetrator and by experiences with multiple versus single perpetrators remains limited. Future work should focus on measurement methods that address these issues without overburdening respondents.

Although this is a wide-ranging review of research on PV, several features of the review should be noted as they may limit the generalizability of the findings. First, studies on constructs that are similar to PV, such as cumulative trauma exposure, multiple victimization, and repeated victimization, were not included because these studies typically assess victimization as well as other types of traumatic but non-victimization events, such as disasters and bereavement. Similarly, studies that expand the concept of PV from individual victims to victimized groups (e.g., family PV) were outside the scope of our review. Future reviews of these areas of study may further inform understanding of co-occurring victimization and potentially traumatic events. In addition, comparisons of PV assessment methods and definitions in relation to prevalence estimates and associated outcomes were beyond the goals of this study but would be useful extensions of this review. Finally, due to cross-national differences in legal definitions and cultural perceptions of violence and victimization, our study was limited to research on U.S. samples. Future research comparing U.S. and international samples is needed to determine the generalizability of our results.

In conclusion, the construct of PV is critical to advancing understanding of the impact of victimization on health and well-being across the lifespan and to improving outcomes for victims. Rather than placing victimization types in research, practice, and policy silos, survivors may be better served by a holistic approach that accounts for cumulative exposure to victimization across the life course, as such an approach is likely to represent a more accurate picture of a survivor’s lived experience of victimization. Funding structures must also advance to better support integrated research, services, and intervention efforts to meet the needs of poly-survivors. Additional longitudinal studies, studies on adult samples, and studies on moderators of PV as well as associated protective factors are needed to address current gaps in the literature. Together these lines of research have the potential to inform public health approaches to violence prevention as well as interventions and treatments to improve outcomes in poly-victims.

Footnotes

Authors’ Note

The opinions and assertions expressed herein are those of the author(s) and do not reflect the official policy or position of the Uniformed Services University of the Health Sciences or the Department of Defense. The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions, or policies of The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government.

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 iDs

Christin M. Ogle

Caroline Harmon-Darrow

Lisa Fedina

Bethany L. Backes

Author Biographies

Christin M. Ogle, PhD, is an assistant research professor at the Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University of the Health Sciences. Dr. Ogle’s research is focused on the impact of early life traumatic experiences on health and development across the lifespan.

Caroline Harmon-Darrow, PhD, MSW, is an assistant professor at Rutgers University School of Social Work, where she researches community conflict resolution, decarceration and police diversion, and the prevention of child trafficking. A community mediator of 24 years, she trains, mentors, and evaluates other mediators, and has held leadership positions in local, state, and national conflict resolution organizations.

Lisa Fedina, PhD is an assistant professor at the University of Michigan School of Social Work. Her research investigates the connections between forms of violence across the lifespan (e.g., child maltreatment, intimate partner violence, sexual assault), health, and mental health outcomes.

Darlene Nichols, MA, MLS, is a research and reference librarian who specializes in social work. She has worked as a librarian for 38 years at the University of Michigan.

Carrie F. Mulford, PhD is the Deputy Branch Chief of the Services Research Branch within the Division of Epidemiology, Services, and Prevention Research at the National Institute of Drug Abuse. Dr. Mulford oversees a range of research projects related to the adoption of evidence-based drug treatment practices, including research on the intersection of exposure to violence and substance use disorders.

Bethany L. Backes, PhD, MSW, MPH, is an associate professor in the Violence Against Women Cluster at the University of Central Florida with appointments in the Department of Criminal Justice and School of Social Work. She studies gender-based violence and crime victimization including formal and informal help-seeking trajectories, secondary and tertiary violence prevention, and criminal justice and community-based interventions.

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