A Person-centered Approach to Violence Exposure in Postwar Colombian Youth: Demographic Covariates and Positive Youth Development Outcomes

Abstract

While the negative impact of extensive exposure to community violence and armed conflict is known, less emphasis has been focused on outcomes supportive of resilience. It is critical to begin exploring these constructs to both promote healing from decades-long conflict and to inform targeted interventions that focus on positive youth development in contexts of adversity. This study thus utilized a person-centered approach to estimate violence exposure profiles among 3,443 Colombian youth to explore what demographic covariates and positive youth development outcomes, such as school engagement, hope, goals, social competence, future expectations, and barriers to education were associated with each violence exposure profile. Four profiles emerged: a low exposure profile, a high community violence profile, a some combined exposure profile, and a high combined exposure profile, each with various levels of community violence witnessing and victimization as well as armed conflict exposure. Demographic covariance results showed older, urban, male youth were more likely to be in the high violence exposure profiles compared to the low exposure group. Youth in the high combined exposure profile were more likely to have lower hope, educational expectations, and social competence compared to the low exposure group. Findings highlight that a person-centered approach provides a more multidimensional view of adolescent violence exposure. Demographic differences suggested the importance of tailoring violence prevention initiatives to the local context. Finally, results concerning positive youth development outcomes suggest that resiliency-oriented constructs, which can be instrumental toward youth’s postwar healing and growth, should be emphasized among populations who experience high levels of co-occurring exposure.

Keywords

community violence violence exposure war youth violence

Introduction

Adolescents in Colombia have been exposed to various types of violence, including community violence (witnessing and victimization) and political violence from decades-long armed conflict in the country. Researchers have documented various rates of youth violence exposure (Velez-Gomez et al., 2013), but little attention has been given to how different types of exposure co-occur and how demographic variables (i.e., SES, urbanicity, age, and gender) covary with exposure type and prevalence among youth. In addition, while researchers document associations between violence exposure and adolescent aggression, mental health, and academic achievement (Ardila-Rey et al., 2009; Kliewer et al., 2001), less is known about how violence exposure affects positive youth development outcomes that may be associated with post-traumatic growth (Masten & Narayan, 2012; Tedeschi & Calhoun, 2004). A resiliency framework posits that children exposed to extreme trauma can withstand and recover from adverse circumstances and go on to experience stability, viability, and positive development (Masten, 2014), and post-traumatic growth holds that positive psychological change can result from highly challenging life circumstances (Tedeschi & Calhoun, 2004). The purpose of the current study was to identify profiles of violence exposure among youth in post-war Colombia to better understand how demographic characteristics, as well as positive youth development constructs such as hope, educational goals and expectations, and social behaviors are associated with each profile of exposure.

Theory and Approach

Ecological systems theory (Bronfenbrenner & Morris, 2007) posits that many influences including individual-level traits and family- and community-level factors directly and indirectly influence child development. In this study, we aim to show how socioeconomic status (SES; microsystem factor) and urbanicity (exosystem factor) as well as individual factors, including age and gender, may lead to adolescents’ membership in unique profiles of violence exposure. In contrast to a variable-centered approach, which operates under the assumption that adolescents are drawn from a homogenous population and experience the variables of interest uniformly (Laursen & Hoff, 2006; Magnusson, 2003), a person-centered approach allows the classification of unique subsets of adolescents based on individual circumstances and then predicts outcomes from shared group similarities. Extant violence research using variable-centered approaches can be complemented through a person-centered approach as it offers a more nuanced understanding of which youth are experiencing co-occurring violence, and additionally how unique groups of youth compare to one another on outcomes. In line with other person-centered studies on exposure to community violence among urban African American youth (Gaylord-Harden et al., 2016; Lambert et al., 2010), this study moves beyond typical variable-centered approaches to identify patterns and variability in exposure, accounting for co-victimization.

Types and Determinates of Violence Exposure

As noted, for over 50 years, the country of Colombia has been in a state of complex armed conflict and sustained, substantial community violence. Indeed, Velez-Gomez et al. (2013) found that in one Colombian city, over 40% of the population had witnessed a homicide, 75% had witnessed an aggressive incident, and 40% had been victims of some other violent act. Colombian youth have experienced multiple kinds of violence exposure, including armed conflict inherent to state and guerrilla warfare, as well as more proximal community violence (Chaux, 2002). Armed conflict refers to a period of fighting that includes a stated incompatibility between organized groups (one of which must be the government of the state) that includes violent activity that has resulted in deaths (Kreutz, 2010). Armed conflict often includes a minority armed insurgency that uses intense military-like actions to control populations and territories (Chernick, 1988). Community violence can comparatively be defined as threats or instances of interpersonal harm within one’s more immediate community or neighborhood (Kennedy & Ceballo, 2013). Youth may witness or be direct victims of community violence, or it is likely that they experience both. There is a spillover effect from armed conflict to community violence as large-scale conflict leads to instability and poverty, which in turn catalyze youth involvement in gangs, drug-trafficking, and petty crime (Llorente et al., 2005). Thus, it is important to understand the possible effects of co-occurrence on youth. As noted above, person-centered approaches are particularly useful for identifying how violence may co-occur differentially for subsets of adolescents.

In addition to identifying such profiles, it is also important to recognize that adolescents’ individual and contextual characteristics may be associated with profile membership. Having a more complex view of how child characteristics vary with exposure can identify which adolescents are in the greatest need of concerted future studies, services, and interventions. Evidence from previous research in Colombia and other countries suggests that demographic characteristics such as SES, urbanicity, age, and gender likely influence amount and type of youth violence exposure. Indeed, SES has been inversely related to aggressive behavior and violence exposure since higher SES parents likely have more protective resources, including access to safer neighborhoods, stability in income and housing, and more support in childcare (Bradley & Corwyn, 2002). Regarding urbanicity, political-historical data shows that much of the guerrilla warfare took place in rural areas of Colombia at the height of the conflict (Restrepo & Spagat, 2004). However, urban areas experienced bombings and large groups of displaced children and families from rural communities relocated to poor shantytown areas on the outskirts of major cities in attempts to protect their lives from terrorism attacks in rural lands (Ardila-Rey et al., 2009). This could mean that exposure to armed conflict was greater in rural areas, but that exposure to community violence was prevalent in urban districts harboring large amounts of refugees with limited resources. Regarding age and gender, rates of violent activity are consistently higher among males and older adolescents across multiple countries including Colombia (Brook et al., 2003; Moffitt, 1993; Rodriguez & Sanchez, 2012; Voisin et al., 2015), and illegal armed groups have recruited male minors as child soldiers (Human Rights Watch, 2003). Further, adolescent males are typically more involved in gang violence than females (Thornberry et al., 2003). The present study attempts a closer look at how each demographic factor mentioned predicts membership in the diversified exposure profiles that emerge.

Negative Effects of Violence Exposure

Young people are thought to be at increased risk of exposure to community violence (Aisenberg & Herrenkohl, 2008), and are especially vulnerable to its negative effects because they are in a rapid period of development where decisions may have lasting implications for life course trajectories (Garbarino, 1997). Both direct and indirect exposure to violence can have negative effects on the developing brain (Guerra & Dierkhising, 2011). Youth encountering armed conflict in Colombia specifically were more likely to condone aggressive action and retaliation, have reduced mental and socio-emotional health, and have poorer academic performance in comparison to unexposed peers at school (Ardila-Rey et al., 2009; Kliewer et al., 2001). Colombian youth who witnessed a crime were more likely to exhibit aggressive behaviors (Chaux et al., 2012), possibly due to justification of violence, poor parenting, and exposure to deviant peers (Caicedo & Jones, 2014). Additionally, armed conflict exposure has been directly and negatively associated with developmental competence (e.g., cognitive abilities, positive behavioral engagement, and socio-emotional well-being; Gaias et al., 2019; Masten & Narayan, 2012). Some researchers have looked into the detrimental impacts of both armed conflict and community violence among Colombian youth. For example, Velez-Gomez et al. (2013) found Colombian youth were more likely to be witnesses than victims of acts of violence and Gaias et al. (2019) found both community violence witnessing and victimization were positively related to adolescents’ externalizing behaviors.

Positive Youth Development Constructs and Violence Exposure

Compared to work on negative outcomes associated with violence exposure, there is significantly less research on positive youth development constructs. This is unfortunate given school engagement, social competence, and future-oriented constructs such as hope, goal-setting, and educational expectations are associated with coping, achievement and well-being (Lerner et al., 2009; Masten, 2014; Wang & Eccles, 2012), and may both support resilience (Tedeschi & Calhoun, 2004). In one of the few studies on the subject, Borofsky et al. (2013) found community violence exposure directly and indirectly predicted poorer GPA through reduced school engagement. Internalizing and externalizing symptoms mediated the relationship, showing adverse psychological effects associated with violence exposure later affected engagement at school. Similarly, social competence was negatively associated with both witnessing and being a victim of violence (Cedeno et al., 2010; Weaver et al., 2008), which aligns with literature evidencing violence victimization as one of the most prevalent predictors of antisocial behavior among youth (Blum et al., 2003). Indeed, low social competence often serves as a risk factor for future delinquent behavior (Sørlie et al., 2008). Greater adolescent social competence may be of particular importance in postwar Colombia. Building collegiality, abiding by school rules, and respecting others’ opinions may assist in improving social relations and community accord. Students who feel respected and avoid disruptive behaviors have higher achievement and aspire for higher education (Akey, 2006; Wang et al., 2010; Wentzel et al., 2010). Thus, knowing how violence exposure co-occurrence affects educational attainment and social competence may lend insight toward interventions aimed at building these beneficial assets and fostering resilience.

It is additionally important to understand how violence exposure co-occurrence affects positive youth development constructs such as hope, goals, and higher educational expectations as they have been linked to greater adolescent well-being, coping, life purpose, and academic achievement across adolescence (Bryce et al., 2020; Chang, 1998; Feldman & Snyder, 2005; Levi et al., 2014; Marques et al., 2017; Valle et al., 2006). Notably, higher educational attainment has been associated with lower odds of poor health in Colombia (Hurtado et al., 2011). Further, hope has been a greater indicator of well-being than community violence in South Africa (Savahl et al., 2013) and can serve as a resilience mechanism within challenging contexts. Previous studies that focus on single types of violence exposure have shown negative effects on future aspirations, including educational plans and hopes for long life expectancy (Stoddard et al., 2015; Warner & Swisher, 2014), though some evidence is mixed or nuanced. Negative relations may seem intuitive because students who are living in a context of violence must spend more mental energy attending to current physical safety rather than future planning. It could also be that students who have been victimized are lacking in the internal agentic beliefs inherent to hope and goal-setting because physical and/or emotional trauma is detrimental to self-worth, self-efficacy and internal locus of control (Copeland-Linder et al., 2010; Dupéré et al., 2012; Shoelds et al., 2008). Interestingly, some studies found political violence exposure was related to higher social adjustment and coping skills (i.e., post-traumatic growth; Rousseau et al., 2003), which are notably related to cognitive-motivational constructs such as hope. Other studies of Palestinian and Israeli-Palestinian youth exposed to terror found individual differences, including subjective perceptions of physical threat, play a part in determining whether violence exposure sways future aspirations and expectations (Lavi & Solomon, 2005). Person-centered research may additionally illuminate the complex nature of resilience in contexts of violence as it can account for numerous profiles of exposure. Indeed, the effects of co-occurring exposure to violence has had limited attention, particularly outside the United States.

Current Study

Processes of post-traumatic growth may be impacted by the resilient actions and traits of Colombian youth, including their school engagement, social competence, hope, goals, and educational expectations. The current study employs a person-centered approach to garner a more complete picture of violence exposure among Colombian youth, with emphasis on investigating whether certain groups of youth were subject to varying types and prevalence of violence. A multidimensional view of exposure may inform targeted intervention efforts where specific groups are identified as higher risk and beneficial competencies can be fostered according to context. Therefore, the current study aims are (a) to use a person-centered approach to estimate Colombian youth violence exposure profiles, including witnessing community violence, being a victim of community violence, and armed conflict exposure; (b) to explore how age, gender, SES, and urbanicity are associated with violence profiles; and (c) to understand how violence exposure profiles are associated with school engagement, social competence, hope, goals, educational expectations, and perceived barriers to future education.

Based on previous LCAs on youth violence exposure (Gaylord-Harden et al., 2016; Lambert et al., 2010), groups high in community violence victimization were expected to also score high in witnessing. Additionally, high and low armed conflict exposure groups were expected as many youths had not lived in areas where armed conflict took place. Males and older adolescents were expected to have higher violence exposure, particularly to armed conflict, than girls and younger adolescents. Profiles with higher witnessing community violence and armed conflict exposure were expected to be associated with reduced social competence, school engagement, and future-oriented traits (Chaux et al., 2012; Gaias et al., 2019; Masten & Narayan, 2012).

Method

Participants and Procedure

Data were collected from 3,443 (age 9-19 years, M =14.2, SD = 2.8) 6th-11th grade students (53% female) in 12 public high schools (six urban, six rural; 53% urban students) in and near the Bolívar province of Colombia, which is in the coastal region of Colombia. Urban schools were located in the city of Cartagena, which is the fifth largest city in Colombia. Rural schools were located in the nearby Montes de Maria region. Schools were found through word-of-mouth and existing social connections between educators and service providers in the Bolívar area. Therefore, schools were located in similar cultural contexts, though community partners supporting recruitment recruited schools that were diverse in areas of SES, urbanicity, crime rates, and neighborhood characteristics. Average parent level of education was high school completion, with responses ranging from did not finish primary school to finishing postgraduate work. Data were collected from 64 class groups with 79.7% participation on average. Only 1.5% of parents opted their children out of study participation, and 1.3% of students did not provide assent; all other students who did not participate were either absent during the day of data collection or no longer enrolled in the school. Each student completed a 20- to 60-minute anonymous paper-and-pencil questionnaire over one or two school days. Each school, as well as a university Institutional Review Board, approved recruitment procedures. Participating schools received a contribution to a school improvement project as remuneration (e.g., recycling bins, printers).

Measures

Translation procedures. All measures were translated into Spanish, taking care to establish semantic equivalence (Knight et al., 2009). Independent bilingual translators translated measures into Spanish, and then back to English and a review team checked for culturally appropriate wording and phrasing. In order to ensure that adaptations were suitable, the survey was then piloted in a school with similar demographics and characteristics of the target sample and appropriate adjustments were made.

Demographic covariates. Students reported on their age, gender (Female = 0, Male = 1), and mothers’ and fathers’ education level (1 = did not finish primary school, 7 = finished graduate school). High school location was used to determine urbanicity.

Exposure to violence. Victimization was measured using the victimization scale: Neighborhood subscale (Nadel et al., 1996), which included seven items (e.g., “In your neighborhood, how often have you been a victim of an attack with a knife or sharp weapon”) utilizing a 4-point scale (0 = never to 3 = many times). The Children’s Exposure to Community Violence scale (Richters & Martinez, 1990) assessed witnessing, which included 17 items (e.g., “In your neighborhood, how often have you seen drug deals?”) and utilized a 4-point scale (0 = never to 3 = many times). Armed conflict exposure was measured using the Childhood War Trauma Questionnaire: Adapted (Macksoud, 1992), which employs 16 items asking whether adolescents had experienced various conditions (e.g., home bombarded, encountered guerrillas, kidnapping). Indices of victimization, witnessing, and armed conflict exposure were summed to create scores. Because 98% of children reported five or less armed conflict situations, scores over five were truncated to five. Scores were standardized (turned into z-scores by subtracting the mean and dividing by the standard deviation) to account for scale differences.

Positive youth development outcomes. Positive youth development outcomes were taken from the Child Trends Flourishing Children Project (Lippman et al., 2014). Social competence assessed positive skills surrounding group collaboration and getting along well with others (six items, e.g., “Do you respect other points of view, even if you disagree?”; α = .76). School engagement assessed how much students cared about and invested in academic tasks (three items, e.g., “I think the things I learn at school are useful”; α = .74). Goals referred to one’s future plans and steps to be taken toward them (five items, e.g., “I have goals in my life.” α = .77). For this study, hope was characterized as a general positive expectations and feelings toward the future (three items, e.g., “I am excited about my future.”; α = .86). Future educational aspirations referred to future expectations regarding educational attainment (two items, e.g., “I will go to university after I graduate from high school.”). Adolescents reported perceived barriers to education by reporting on various obstacles to achieving their preferred level of education (seven items, e.g. “I lack the talent or skills to follow my first career choice or continue my education.”). Measures utilized four-point scales (1 = totally disagree, 4 = totally agree) and means were created by averaging item scores.

Analytic Strategy

Preliminary analyses. Preliminary analyses including descriptive statistics, frequencies, and correlations were conducted using SPSS 25. Univariate normality and outliers were assessed by examining descriptive statistics, histograms, and frequency charts.

Hypothesis testing. We used latent profile analysis (LPA; Hagenaars & McCutcheon, 2002; L. K. Muthén & B. Muthén, 2019), which estimated profiles iteratively, adding one profile at a time, to identify the optimal number of violence exposure profiles. The LPA approach identifies nonobservable profiles of individuals based on similar patterns of responses across multiple, observed, continuous indicators. The LPA was run using the Maximum Likelihood Robust estimator in Mplus 8.3, which accounts for multivariate non-normality (L. K. Muthén & B. Muthén, 2019). A number of fit statistics, including the AIC, BIC, adjusted BIC, and Lo-Mendell-Rubin adjusted test, were employed to discern the appropriate number of profiles. The AIC and BIC take sample size, model fit, and number of model parameters into account. The model with the lowest AIC and BIC is considered to have the most optimal fit. The VLMR-LRT, LMR-LRT, and BRLT allow for nested model comparison utilizing chi-square difference testing, with significant p-values suggesting a given solution has significantly better fit than the previously one (Nylund et al., 2007). In addition to evaluating fit statistics, conceptual rational and preference for parsimony were used in determining which model was optimal (Asparouhov & Muthén, 2014; Nylund et al., 2007).

After choosing the best LPA model, the three-step method (R3STEP; Asparouhov & Muthén, 2014; Vermunt, 2010) was employed to investigate how demographic characteristics (i.e., age, gender, SES, and urbanicity) were associated with membership in each profile. The R3STEP method estimated latent profiles (Step 1) and then created a “most likely” or “nominal” variable using the LPA’s posterior distribution that represents which profile each individual was most likely to belong to, accounting for measurement error (Step 2). Step 3 utilized multinomial regression to estimate which demographic characteristics significantly predicted latent profile measurement.

Next, we examined whether positive youth development outcomes (i.e., school engagement, social competence, hope, goals, educational expectations, and perceived barriers to education) differed by youths’ probabilities of assignment to different violence exposure profiles. We used the Bolck, Croon, and Hagenaars (BCH) approach (Bakk & Vermunt, 2016; Bolck et al., 2004; Croon, 2002; Vermunt, 2010) to predict outcomes from latent profiles. The BCH method employs a weighted multiple group analysis, where weights represent measurement error of the latent profile variable. After conducting the LPA, the measurement error for the most likely profile variable was determined (Asparouhov & Muthén, 2014). Finally, the LPA was estimated again using the most likely profile variable, fixing measurement error of the most likely profile to the values previously computed. The BCH method then tested for mean differences in positive youth development outcomes across profiles. Despite the strengths of the R3STEP, BCH processes, models utilize list-wise deletion for cases where any data are missing. Missing data ranged from 15% to 27% across analyses (varied by missingness on each specific predictor and outcome). In consideration of this limitation, we ran differential missingness analyses. Results indicated that students who were excluded from analyses due to missing data did not vary significantly from those included on all violence indicators, allowing us to assume data were missing at random.

Results

Research Aim 1. Latent Profiles of Violence Exposure

Descriptive statistics for all variables including zero-order correlations, means, and standard deviations can be seen in Table 1. Using the three standardized indicators of violence exposure, witnessing community violence, being a victim of community violence, and being exposed to armed conflict, we fit models ranging from two to six classes extracted (class size became restrictive with more groups). The best fitting solution included four profiles and had lower AIC, BIC, and adjusted BIC indices than the three-class model and lower entropy than the five and six class models (see Table 2 for fit statistics). Additionally, the five-class model did not enhance theoretical meaning as it seemed to split the high CV exposure group seen in the four-class model into two and resulted in a class with only 2% of the sample in it. The resulting four profiles (Figure 1) in the best-fitting model comprised (1) a low exposure group (N = 225, 74%) characterized by relatively low witnessing, low victimization, and nearly no armed conflict (2) a high CV exposure group (N =139, 4%) characterized by the highest amounts of witnessing and victimization, and nearly no armed conflict (3) a some combined exposure group (N = 454, 13%) characterized by moderate witnessing, some victimization, and some of armed conflict, and (4) a high combined exposure group (N = 300, 9%) characterized by high witnessing and victimization, and the highest armed conflict. The posterior probabilities, indicating the likelihood of being correctly classified within each profile, were .99, .89, and .97, and .95 for the four profiles, respectively. Posterior probabilities are reported on a scale of 0 to 1 and are useful in ensuring accuracy of class assignment within every group. Lanza and colleagues (2007) note, “An average close to one for the assigned class suggests that one can have high certainty about true class membership for those individuals” (p. 684). Thus, our probabilities were proficient in determining accuracy.

Figure 1.

Violence Exposure (Standardized).

Table 1.

Descriptive Statistics and Zero-order Correlations Among Study Variables.

	1.	2.	3.	4.	5.	6.	7.	8.	9.
1. Witnessing community violence	1
2. Victim of community violence	.47***	1
3. Armed conflict exposure	.28***	.31**	1
4. School engagement	-.02	-.04**	-.03	1
5. Social competence	-.08***	-.09***	.08***	.28***	1
6. Hope	-.05*	-.07***	-.06**	.46***	.24***	1
7. Goals	-.03	-.06**	-.04*	.35***	.20***	.50***	1
8. Future expectations for education	-.05**	-.07***	-.04*	.41***	.21***	.53***	.55***	1
9. Perceived barriers to education	.05**	.06**	.09***	.00	-.09***	-.05**	.02	-.05**	1
N	3,395	3,347	3,141	3,214	3,352	3,147	3,160	3,194	3,158
Mean	12.96	2.15	.92	3.57	4.07	3.60	3.84	3.70	2.46
SD	8.35	2.89	1.43	.53	.80	.58	.43	.54	.78

Note. *p < .05, **p < .01, ***p < .001.

Table 2.

Fit Statistics for Latent Profile Analysis of Violence Exposure.

Classes	Log Likelihood	AIC	BIC (Adj. BIC)	LMRLRT p Values	Entropy	Class Proportions
2	−.12826.235	25,672.47	25,733.91 (25,702.14)	<.001	0.91	2,936 (85.3%) 507 (14.7%)
3	−.12205.055	24,438.11	24,524.13 (24,479.64)	<.001	0.94	2,674 (77.7%) 464 (13.5%) 305 (8.9%)
4	−11,804.728	23,645.46	23,756.05 (23,698.86)	<.001	0.93	2,550 (74.1%) 139 (4.0%) 454 (13.2%) 300 (8.7%)
5	−11,251.110	22,546.22	22,681.39 (22,611.49)	<.001	0.94	2,034 (59.1%) 532 (15.5%) 467 (13.6%) 308 (8.9%) 102 (9.7%)
6	−11,246.869	22,545.74	22,705.48 (22,622.87)	0.01	0.94	2,500 (72.6%) 380 (11.0%) 93 (2.7%) 173 (5.0%) 232 (6.7%) 65(1.9%)

Note. AIC = Akaike information criteria; BIC = Bayesian information criteria; adj BIC = sample-size adjusted Bayesian information criteria; LMR = Lo-Mendell-Rubin; LRT = likelihood ratio test. The best fitting class solution is noted in bold.

Research Aim 2. Demographic Indicators of Violence Exposure Profile

Results from the R3Step portion of the LPA analyses demonstrated that as age increased, adolescents were more likely to be in any profile other than the low exposure profile; urban youth were more likely to be in the high CV profile than the low exposure profile; and as SES increased, adolescents were less likely to be in the high combined exposure profile compared to the low exposure profile. Males were more likely to be in the high combined profile compared to the low exposure profile.

Research Aim 3. Resiliency-oriented Outcomes Associated with Violence Exposure Profiles

Results for the BCH portion of the LPA analyses determining differences in scores on positive youth development outcomes by profile group can be seen in Table 3. Notably, school engagement and goals did not significantly differ among profiles. Students in the high CV, some combined, and high combined exposure profiles showed significantly lower social competence than students in the low exposure group. The high combined exposure profile scored significantly lower in hope compared to the low exposure profile. The high CV and high combined exposure profiles scored significantly lower in future expectations for education compared to the low exposure profile. Students in the high combined profile perceived more barriers to future education than students in the low and some combined exposure profiles.

Table 3.

Mean Differences in Positive Youth Outcomes Across the Latent Profiles.

	School Engagement	Social Competence	Hope	Goals	Future Expectations for Education	Perceived Barriers to Education
Low Exposure¹	3.58 (.01)	4.12 (.02)^2,3,4	3.62 (.01)⁴	3.86 (.01)	3.72 (.01)^2,4	2.43 (.02)⁴
High Community Violence²	3.59 (.06)	3.87 (.09)¹	3.61 (.05)	3.81 (.05)	3.56 (.07)¹	2.53 (.08)
Some Combined Exposure³	3.53 (.03)	3.97 (.04)¹	3.56 (.03)	3.80 (.02)	3.68(.03)	2.45 (.04)⁴
High Combined Exposure⁴	3.56 (.03)	3.94 (.05)¹	3.52 (.04)¹	3.81 (.02)	3.64 (.04)¹	2.67 (.05)^1,3

Note. Superscripts following a mean score indicate significant differences on the chi-square test of independence at the p <.05 level between the profile row and the subscript denoted (1 = low exposure, 2 = high community violence, 3 = some combined exposure, 4 = high combined exposure).

Discussion

This study employed a person-centered approach to better understand the complex nature and implications of Colombian youth’s experiences of violence. Analyses explored whether demographic characteristics (i.e., age, gender, urbanicity, and SES) covaried with profile membership and investigated whether positive youth development outcomes (i.e., school engagement, social competence, hope, goals, educational expectations, perceived barriers to future education) were differentially associated with the violence exposure profiles. Results revealed four violence exposure profiles. Most students (74%) were categorized into the low exposure group; however, the other 26% of students had been exposed to higher levels of multiple types of violence. Older, urban, male youth were more likely to be in the high violence exposure profiles. Youth in the high exposure profiles were significantly more likely to have lower hope, educational expectations, and social competence compared to the low exposure group.

Research Aim 1. Broadening the View of Violence Exposure

The present study adds to current knowledge of violence exposure as results indicated Colombian youth were exposed to multiple kinds of violence in varying degrees. While this may be intuitive considering Colombia’s history of armed conflict, using a person-centered analysis to identify profiles of violence exposure demonstrates complexity in youth’s experiences of violence. The majority of youth were classified into the low violence exposure group; however, it is important to note that this group scored low compared to other groups, and still averaged at least 11 counts of witnessing violence and at least one count of victimization, which is on par with studies done in the United States, showing that up to 80% of youth in high-risk neighborhoods are exposed to violence in their homes, neighborhoods, and schools (Fowler et al., 2009; Stein et al., 2003). Although community violence was prevalent in the current study, it could be that adolescents in this group were sheltered from extensive exposure through socialization factors (e.g., high monitoring by parents), or by developing strategies to minimize contact with areas where violence was most centralized. The lack of armed conflict was less surprising, as the height of the conflict in the region of data collection happened approximately 10 years before this study commenced. Over a quarter of the sample in this study (N = 893) had considerably higher rates of exposure, including armed conflict exposure. The high combined and some combined exposure profiles, which contain notable amounts of armed conflict exposure, potentially evidence the spillover effect Chaux (2002) spoke of, suggesting interrelatedness between large-scale political conflict and community violence. Another possible mechanism could be through intergenerational trauma, in which exposure of parents influences family-level violence (Mejia et al., 2006). As domestic violence is one of the largest social issues in South America (Flake & Forste, 2006), future work should tease apart different predictors and outcomes associated with family-level as well as community-level violence, as they may be interconnected and add another layer of complexity to youth well-being.

Additionally, it appeared that community violence victimization was coupled with relatively similar amounts of witnessing in the high CV, some combined, and high combined groups. Indeed, in line with Lambert and colleagues LCA findings (2010), no “witnessing only” group emerged in the current LCA, perhaps signaling how youth who were personally victimized were additionally exposed to the victimization of others through witnessing. Previous work on the effects of community violence victimization versus witnessing is mixed, with Lynch and Cicchetti (1998) finding that victimization was related to youth stress, depression, and low self-esteem where witnessing was not. Conversely, Jonasz and colleagues (2008) found that witnessing was a stronger predictor of adolescent externalizing problems than victimization. Similar to Gaylord-Harden et al.’s (2016) LCA findings that some groups of US youth experience low, high, and moderate amounts of concurrent witnessing and victimization, present findings suggest researchers should consider that many youths have been exposed to both witnessing and victimization and effects may be interconnected.

Research Aim 2. Understanding Demographic Indicators and Violence Exposure

Drawing on Bronfenbrenner and Morris’ (2007) premise that individual and environmental factors will influence the experiences and development of unique children, results showed that variations in age, gender, SES, and urbanicity all contributed to experiences of violence. The R3Step analysis showed that older adolescents were more likely to be in higher violence exposure profiles. Older adolescents are more likely to be in gangs, spend time with delinquent peers, and have less supervision from parents compared to younger children, and therefore may have more time and opportunity to be exposed to community violence (Ferguson & Meehan, 2011). Results may also evidence a cohort effect as older adolescents in this sample may have directly experienced armed conflict events due to the timing of the height of the conflict in the region where data were collected (mid-2000s) relative to when data were collected (2016). Male students were also more likely to be in the high combined exposure group compared to the low exposure group, aligning with previous work showing higher rates of involvement in and exposure to violence for males relative to females (Voisin et al., 2015). Not surprisingly, high SES youth had lower odds of being in the high combined exposure group compared to the low exposure group, supporting the association between increased resources and protective factors. Interestingly, there were not significant differences by SES between the low exposure group and the high CV and some combined violence profiles. We take this to mean that regardless of SES, many youths were unable to escape exposure to at least some amounts of armed conflict or to high rates of community violence. Likewise, urban youth were more likely to be in the high CV profile than the low exposure profile. This shows that violence exposure occurred in metropolitan areas (where SES is usually higher), possibly due to spillover effects from the armed conflict, but likely also due to community violence being more prevalent in urban versus rural areas because of denser population and prevalence of poverty (Stein et al., 2003). Overall, findings support broad interventions, as well as greater attention to older, male, urban, and low SES youth as targets for clinical intervention and services considering the high levels of co-occurring violence they have experienced.

Research Aim 3. Violence Exposure and Positive Youth Development Outcomes

Because positive youth development is relevant for both individual growth and community-building, it becomes important to identify groups where positive development is in the most need of attention. Although previous variable-centered studies demonstrate negative relations between violence exposure and school engagement (Barofsky et al., 2013), we did not find that relation in our data. Groups did not significantly differ on measures of school engagement. While this could be due to the school-based nature of our sample, it could also be that students chose to engage in supportive school environments as a coping mechanism, actively separating their school experiences from their violence exposure. This supports positive school climates as a potentially effective resource in the resilience process with the potential to ameliorate some of the influence of trauma due to violence exposure on resiliency-oriented outcomes, as other researchers have posited (Gaias et al., 2019; Masten, 2014).

In contrast, groups of youth in the high CV, some combined, and high combined profiles demonstrated significantly lower social competence than the low exposure group. This finding highlights the pervasive, deleterious effect of any violence exposure on positive interpersonal skills including collaboration and pragmatic conflict resolution with others. If youth see and experience multiple forms of violence as the dominate means of power attainment modeled in their environment, and are experiencing trauma due to chronic violence exposure, they may be unable to obtain social skills that prioritize egalitarianism, perspective-taking, and compromise. This effect is well-documented in contexts of war and poverty, where violent means of control led to widespread distrust in others, competition for limited resources, and traumatic stress that countered the acquisition of interpersonal skills (see Bell & Jenkins, 1991; So, 2019). This finding suggests a need for interventions that foster recovery and/or development of social skills in safe, supportive environments. School may be a prime context in which to deliver such interventions, especially considering the finding that school-related outcomes were not detrimentally impacted by violence exposure. In fact, school-based interventions have demonstrated success in promoting social competence for youth exposed to violence (Lochman & Wells, 2004).

The high combined exposure groups scored lower on hope compared to the low exposure profile, though this was not the case for goals. It could be that youth were able to think of goals for the future, yet they were unable envision how those goals might be attainable given the traumatic, unstable, and/or discouraging nature of the present (Hilley et al., 2019). Indeed, Snyder et al. (1997) theorized that having hope and seeing ways around obstacles required advanced cognitive skills to construct viable pathways toward goals as well as the agentic belief that one’s abilities and circumstances could propel one toward a positive future. Groups of students exposed to chronic, intense violence may have compromised cognitive skills due to trauma as well as reduced efficacy to leverage toward goal pursuit. Thus, it is not surprising that groups of youth exposed to high levels of co-occurring violence showed lower hope. This finding could be driven by the fact that the high combined profile included all three kinds of violence, or alternatively that this profile contained the highest amount of armed conflict exposure.

The high combined exposure and high CV groups scored significantly worse than the low group for future expectations for education. High levels of co-occurring violence exposure (through community violence or community violence combined with armed conflict) represent experiences of trauma that are linked to both internal (e.g., mental health) and external (e.g., feeling unsafe in a learning environment) factors that could get in the way of attending school and achieving academic success. Findings for perceived barriers to education were slightly different, with the high combined group scoring significantly higher than both the low and some combined groups. This result likely speaks to the salient, detrimental nature of high amounts of armed conflict exposure combined with high community violence exposure. The trauma likely diminished resources for youth, including homes, parents’ jobs, or even parents’ lives. The arresting experience of extreme forms of violence likely provides ample evidence that insurmountable challenges (e.g., kidnapping, displacement) may hinder physical and mental abilities to apply for, pay for, and attend school.

Limitations, Implications, and Future Directions

This study was self-reported and cross sectional in nature, and therefore we cannot make causal statements about the developmental effects of violence exposure. This was a convenience sample, which has a number of limitations including the possibility of under and over-sampling the population, bias in terms of who decides to participate (e.g., low SES schools may be in more need of resources offered) and the inability to generalize results to all youth, or even to all youth in Colombia. We note that our sample was limited to one area in Colombia, and the war affected this area of the country differently compared to others. The provinces that we sampled from experienced severe armed conflict events in the early to mid-2000s, with more direct exposure occurring in the rural areas, but considerable displacement affecting the urban area as well. Considering Colombia is a very culturally diverse and heterogeneous country, we invite caution in generalizing the findings to all Colombian youth. Future work should ask more nuanced questions about the location and intensity of violent incidents rather than just type and amount, employ multiple reporters on youth behaviors to prevent bias, and also use longitudinal designs to assess developmental consequences of violence exposure over time. Additionally, these data were collected after the majority of armed conflict in Colombia had ended, which has implications for the age differences as well as prevalence of violence exposure. Our study would have also benefitted from attention to sexual orientation, disability status, religious affiliation, and race/ethnicity, as various marginalized or minoritized statuses may lead to increased risk of violence victimization due to targeted discrimination. It is possible that these characteristics intersect with those examined in this study, with clinical implication for responding to youths’ identities in relation to their context. In addition, armed conflicts and effects of poverty differ by country and culture; thus, we cannot generalize our specific findings to youth in other contexts.

In line with the ecological systems approach that undergirds with this study, we must note that family-level violence is also prevalent in Colombia (Flake & Forste, 2006), and many youths may have been victims or witnessed violence within the walls of their own homes. Although we used validated, established scales to assess community-level violence, it is possible that family-level violence may have introduced some noise into the data. Future work should look more specifically at different predictors and outcomes associated with family-level violence among Colombian youth and parse apart effects of family- versus community-level violence exposure. Additionally, violence exposure, particularly in context of war, could have intergenerational effects that trickle down to current youth from parents and grandparents affected more directly by war (Betancourt, 2015). Although the intergenerational effects of violence exposure are less studied in Colombia, research out of African conflict areas, refugee populations, and American military families show that war can have negative effects on families across time as trauma affects parent mental health, parenting behaviors, and intimate partner violence (Betancourt, 2015; Jones, 2012; Sangalang & Vang, 2017). Notably, family-level interventions have been successful in diminishing family-level violence (Wieling, 2018). Not measuring family-level violence or intervention efforts limits our ability to parse apart family-level from community-level exposure and treatment. This is an area for much needed future research.

Despite the limitations, we believe these findings have important implications for researchers, educators, and practitioners. Taking a person-centered approach to understanding youth violence exposure may lend a more complete view of youth experiences. Our results demonstrated that empirically investigating only one type of exposure, or treating youth for one specific kind of exposure, may be naïve given that all the youth in our sample who had been exposed to violence experienced more than one type. For practitioners and counselors, this implies that clinicians need to consider the various forms of violence that individual youth have experienced. Trauma-informed approaches, which can address both the totality of individual experiences and understand behavior in context may be particularly important to consider (Bulanda & Byro Johnson, 2016). Practitioners should identify ways to support youth who have experienced co-occurrence of violence, and particularly those whose exposure experiences included high levels of armed conflict, which in this sample appeared to be older, urban, low-SES males. In addition, it is important to acknowledge the specific experiences of cohorts of youth, particularly those who live in the context of war. Overall, our findings support the negative implications of youth violence exposure on positive youth development outcomes that could assist youth in overcoming the trauma caused by violence exposure and help build trust and stability among postwar youth. Findings additionally show that using a person-centered approach can offer clues as to the complex nature of violence experience and identify groups of youth with unique needs. Our findings also support schools, in addition to or perhaps in tandem with family settings, as promising milieus for the development and promotion of resilience.

Footnotes

Declaration of Conflicting Interests

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:

This study was funded by the National Science Foundation and the United States Agency for International Development through a fellowship to the second author (DGE-1311230). We additionally recognize the ASU Latino Resilience Enterprise’s integral role in funding and supporting this project.

ORCID iDs

Ashley M. Fraser

Larissa M. Gaias

Author Biographies

Ashley M. Fraser, MS, the overarching goal of Ashley’s research is to understand and support positive development across childhood and adolescence, particularly in contexts of adversity. To do so, she specifically investigates the interplays of individual-, family-, and societal-level factors on the development of hope, empathy, prosocial behavior, and practices that contribute to equitable and inclusive behaviors across childhood.

Larissa M. Gaias, PhD, is an assistant professor of Psychology at the University of Maryland. Her research focuses on the development, implementation, and evaluation of equity-explicit school-based social emotional, behavioral, and mental health interventions. She is interested in understanding how individual and institutional biases in educational contexts can better support marginalized youth, including youth of color and youth affected by violence.

Ana Maria Melendez Guevara’s, MSW, research interests include child and family development in contexts of adversity and equity issues within mental health delivery. Goals include contribution to the development and evaluation of culturally sensitive interventions that address the complex needs and strengths of families by identifying micro and macro mechanisms of resilience and adaptation in service systems, focusing on interventions that embrace trauma-informed approaches.

Sarah Lindstrom Johnson, PhD, is an associate professor in the School of Social and Family Dynamics at Arizona State University. Her research focuses on designing and evaluating interventions to support youth development as a mechanism to prevent involvement in risk behaviors including violence. This work is conducted in partnership with youth serving organization with a desire to create sustainable change.

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