Artículos
Criminal recidivism
in Colombian juvenile offenders: Related risk and protective factors
Reincidencia
delictiva en adolescentes colombianos: factores de riesgo y protectores
relacionados
Arcadio de Jesús Cardona-Isaza arcadiocaris@unisabana.edu.co
Universidad de La Sabana,
Colombia
Ángela María Trujillo Cano angela.trujillo@unisabana.edu.co.
Universidad de La Sabana,
Colombia
Criminal recidivism in Colombian juvenile offenders: Related
risk and protective factors
Interdisciplinaria,
vol. 40, núm. 1, pp. 413-432,
2023
Centro Interamericano de Investigaciones Psicológicas y Ciencias
Afines
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Recepción:
08
Febrero 2021
Aprobación:
14
Octubre 2022
Abstract:
Research with adolescent offenders is
concerned with identifying risk and protective factors that influence
recidivism and desistance from crime. A quantitative and cross-sectional
investigation designed to examine the influence of risk and protective factors
on recidivism in Colombian adolescents is presented. In seven regions of
Colombia, a convenience sample was obtained, and 646 adolescents aged 14 to 19
years (M = 17.08; SD: 1.23; 15 % girls) belonging to the Sistema de
Responsabilidad Penal para Adolescentes (SRPA) participated. The Communities
That Care Youth Survey (CTC-YS) was used for the evaluation. It evaluated a
broad set of risk and protective factors identified through the community,
school, family, peer group, individual conditions, and behavioral outcomes,
including drug use, antisocial behavior, and delinquency. Descriptive analyses
were conducted, and all CTC-YS factors were correlated with antisocial
behavior. The results show varying degrees of relationship between the factors
assessed and antisocial behavior. Binary logistic regression was used to
determine which risk and protective factors influence recidivism. It was noted
that favorable parental attitudes towards drug use and antisocial behavior,
early onset of drug use, low school engagement, and interaction with antisocial
peers increases the probability of recidivism. Recidivism was identified as
being affected by, among other factors, favorable parental attitudes toward
drug use and antisocial behavior, early onset of drug use, and low school
engagement. It was also observed that beliefs in a moral order, opportunities
for prosocial school participation and lower drug use frequency reduce the
probability of recidivism.
According to the results, the factors that influence criminal recidivism are multiple, and social, family, school, and individual factors need to be addressed. The need to intervene in attitudes favorable to antisocial behavior on the part of parents, strengthen school services, and carry out treatment for drug use to favor the reduction of recidivism in Colombian adolescents is discussed.
Keywords: adolescents,
recidivism, antisocial behaviour, risk factors, protective factors.
Resumen: La investigación con adolescentes ofensores busca identificar
los factores de riesgo y de protección que afectan a la reincidencia y al
desistimiento. Esta información es útil para desarrollar programas de prevención
de la conducta antisocial y facilita los procesos de intervención que favorecen
la reinserción social. Desde el punto de vista legal, la reincidencia es la
participación de un individuo en nuevos actos delictivos, que conduce a una
nueva condena, después de haber sido judicializado por un delito anterior. El
desistimiento, en cambio, es la interrupción de la conducta antisocial y se
caracteriza por la reinserción social exitosa y el ajuste a las normas de la
comunidad. Se han identificado factores sociales, familiares, escolares,
relacionales e individuales que afectan a la reincidencia y al desistimiento.
Se presenta una investigación cuantitativa que utilizó una medición de corte transversal, diseñada para examinar la influencia de los factores de riesgo y protección en la reincidencia de los adolescentes colombianos. Se realizó un muestreo por disponibilidad y conveniencia en instituciones de siete departamentos o regiones geográficas de Colombia. Los participantes fueron 646 adolescentes de entre 14 y 19 años (M = 17.08; DT: 1.23; 15 % chicas). Todos ellos estaban judicializados y cumpliendo sus sanciones legales en el Sistema de Responsabilidad Penal para Adolescentes (SRPA).
Para la evaluación se utilizó la encuesta Communities That Care
Youth Survey (CTC-YS). Se trata de un instrumento de 135 ítems diseñado para
medir un amplio conjunto de factores de riesgo y de protección identificados a
través de las condiciones de la comunidad, la escuela, la familia, el grupo de
pares y el individuo, así como los resultados conductuales, que incluyen el uso
de drogas, la violencia, el comportamiento antisocial y la delincuencia. El
instrumento mostró buena fiabilidad en este estudio. La reincidencia se evaluó
con criterios legales, es decir, se tuvieron en cuenta el número de condenas
oficiales. Para ello se revisaron los expedientes de los participantes y se los
cruzó con la información reportada por los profesionales que atendían los
centros y el autoinforme de los participantes.
Se obtuvo la aprobación del comité de ética y permiso del
gobierno a través del Instituto Colombiano de Bienestar Familiar -ICBF-
(Autorización E-2016-660327-0111). Los consentimientos informados fueron
firmados por los defensores, los directores de los centros de atención, los
padres de los adolescentes y por cada uno de los participantes. Una vez
finalizada la investigación, se socializaron los resultados a través de grupos
focales con los interesados, incluidos los adolescentes.
Se realizaron análisis descriptivos con los datos y se
correlacionaron todos los factores del CTC-YS con la variable conducta
antisocial y delictiva provista por el mismo instrumento. Luego se realizó una
regresión logística binaria para determinar qué factores de riesgo y protección
influyen en la reincidencia.
Se observaron diferentes grados de relación entre los factores
evaluados y la conducta antisocial-delictiva. Los resultados indican que la
reincidencia se ve afectada, entre otros factores, por las actitudes favorables
de los padres hacia el uso de drogas y la conducta antisocial, el inicio
temprano del consumo de drogas y el bajo compromiso escolar. Las creencias en
un orden moral y las oportunidades por la participación escolar prosocial y la
menor frecuencia de uso de drogas muestran disminución en la probabilidad de
reincidencia.
Según los resultados, los factores que influyen en la
reincidencia delictiva son múltiples y requieren la intervención de las
condiciones sociales, familiares, escolares e individuales. Se discute la
necesidad de intervenir en las actitudes favorables a la conducta delictiva por
parte de los padres, fortalecer los servicios escolares, realizar tratamiento
para abandonar el uso de drogas y desarrollar modelos de intervención que
cuenten con evidencias de eficacia para ayudar a reducir la reincidencia en los
adolescentes colombianos.
Palabras
clave: adolescentes, reincidencia, conducta antisocial, factores de
riesgo, factores protectores.
Introduction
Recidivism is defined as “the official criminal participation
(based on a legal record) of a person who, after having been convicted of a
previous crime, commits a new offense for which he incurs another conviction” (Zara & Farrington, 2016, p. 5). Youth
recidivism is a problem with social, economic, and public health implications,
and its prevalence varies according to the context (Orlando & Farrington, 2021; Zara & Farrington, 2016). More
knowledge is needed about the risk and protective factors in this population to
determine which young people may be at most risk of recidivism (Piquero, Farrington, Nagin, & Moffitt,
2010;Sousa, Cardoso, & Cunha, 2019).
Thereby provides an understanding of the associated problems and implementing
relevant reentry strategies (Andrews &
Bonta, 2017; Lee, Moon, & Garcia,
2020; Singh, Kroner, Wormith,
Desmarais, & Hamilton, 2018; Stojkovic,
2017).
Studies with adolescents have identified individual, family,
school, and community risks associated with recidivism (Kennedy, Edmonds, Millen, & Detullio,
2019). It has also been noted that there are common risk factors that, when
intervened, help reduce recidivism; for example, criminal history, favorable
attitudes toward criminal behavior, antisocial peers, antisocial personality,
interpersonal relationships, use of leisure time, school (or work), and drug
use (Andrews & Bonta, 2017).
Studies have shown that the risk factors may be different according to the type
of crime (Coupland & Olver, 2020; Grossi, Brereton, Lee, Schuler, &
Prentky, 2017).
It has been suggested that adolescents with a higher risk of
recidivism accumulate a greater number of adverse factors. In this regard, a
cohort study compared the risk and protective factors in adolescents and young
adults. It was observed that those who began their antisocial life at an
earlier age presented cumulative risks in a wide variety of domains, including
school, relationships, peer group, family history of antisocial behavior,
antisocial attitudes, aggression, alcohol use, drug abuse, and a history of
mental health problems (Baglivio,
Jackowski, Greenwald, & Howell, 2014). In juvenile offenders gender
neutrality has been observed in global risk domains, i.e., risk factors predict
recidivism to a similar degree in men and women, particularly in relationships
with antisocial peers, family problems, drug use, antisocial behavior, and
antisocial attitudes (Cuevas, Wolff, &
Baglivio, 2019).
Community risk and protection factors
Community intervention has been reported as favoring desistance
in offenders with disabilities and special educational needs and fostering violent
offenders' social integration (De Vries
Robbé, de Vogel, Douglas, & Nijman, 2015). Evidence indicates that
having job opportunities and living in peaceful neighborhoods, supportive
housing, and social support reduce recidivism (Huebner & Pleggenkuhle, 2015; Pleggenkuhle, Huebner, & Kras, 2016).
In adults, education levels and post-release employment are significantly
correlated with recidivism, regardless of the classification of crimes (Nally et al., 2014). Having support
services for the reentry process in the community better-paying jobs in the
legal economy and making commitments with the personal change in the transition
to adulthood, reduce the chances of recidivism (Chamberlain, Boggess, & Powers, 2016; Schubert, Mulvey, & Pitzer, 2016).
Access to healthy leisure activities and free time engagement in adolescents
has shown to be a protective factor for recidivism (Cuervo Gómez, Villanueva Badenes, &
Pérez Castillo, 2017).
Family risk and protection factors
In the family setting, poor parenting skills, a history of
criminal behavior in the family, dysfunctionality, and the presence of physical
and emotional abuse are predictors of recidivism in adolescents (García-García, Ortega-Campos, & de la
Fuente-Sánchez, 2010; Kennedy,
Edmonds, Millen, & Detullio, 2019; Ortega-Campos,
García-García, De la Fuente Sánchez, & Zaldívar Basurto, 2012).
Research on reentry processes suggests that living with the
family delays recidivism, especially among men, and that living with an
intimate partner may be a predictor of harmful and robust failure for men and
women (Huebner & Pleggenkuhle, 2015).
Recent findings show that childhood physical abuse is related to violent
recidivism in males (van der Put & De
Ruiter, 2016). Furthermore, sibling crime is a risk factor for various
future offenses (Walters, 2018). It
has been suggested that family conflicts may be a stronger predictor of recidivism
than having a relationship with antisocial peers (Mowen & Boman, 2019).
In terms of protective factors, parental control has shown to
reduce risk behaviors for recidivism and drug use (Voisin, Tan, Tack, Wade, & DiClemente,
2012); family support is also a protective factor that favors reentry
processes (Huebner & Pleggenkuhle,
2015;Pleggenkuhle et al., 2016).
School risk and protection factors
Healthy activities related to the use of free time, study and
school have been shown to reduce recidivism. A meta-analysis conducted in Spain
showed that being in school is a protective factor that prevents adolescents
from engaging in delinquent activities (García-García,
Ortega-Campos, & de la Fuente-Sánchez, 2010). Studies performed in
different contexts show that adolescents at higher risk of relapsing into
delinquent behavior have more school problems and lower academic performance (Cacho, Fernández-Montalvo, López-Goñi,
Arteaga, & Haro, 2020; Vaughn,
Salas-Wright, DeLisi, & Maynard, 2014).
On an educational level, it is considered that school
instruction, the implementation of structured academic interventions and
strengthening reading skills can reduce recidivism (Joo & Jo, 2015; Katsiyannis, Ryan, Zhang, & Spann, 2008;Silver, Cochran, Motz, & Nedelec, 2020).
Acquiring academic competence and employability upon graduation has been shown
to positively affect social integration in juvenile offenders (Steele, Bozick, & Davis, 2016).
Academic competence has been widely related to prosocial behavior in the
general population (Jutengren & Medin,
2019). Also, socio-educational support is necessary for adolescents leaving
residential centers to maintain the achievements and skills acquired and
achieve adequate social reintegration (Martínez
Virto, 2021).
Individual and peer group risk and protection factors
Individual factors are the most extensively researched domain in
juvenile recidivism. Evidence indicates that the crime and recidivism rate is
higher among men (Moffitt, 2018).
The individual predictors and those associated with the peer
group with the greatest empirical support are the presence of attitudes
favorable to antisocial behavior (Ngo,
Paternoster, Curran, & Mackenzie, 2011), gang membership (Chu, Daffern, Thomas, & Lim, 2012),
violence in carrying out crime (Khachatryan,
Heide, & Hummel, 2018), association with antisocial peers (Boman & Mowen, 2017; Spruit, van der Put, Gubbels, & Bindels,
2017), and impulsiveness and low levels of anger control (Khachatryan et al., 2018; Navarro-Pérez, Viera, Calero, & Tomás,
2020). Adolescents with behavioral disorders, a history of suicide, and
those who have been exposed to more adverse childhood experiences have been
reported as being more likely to relapse (Mallett,
Fukushima, Stoddard-Dare, & Quinn, 2013; Wolff, Baglivio, & Piquero, 2017).
Regarding mental disorders, the evidence suggests a positive
relationship between recidivism and externalizing disorders, such as drug use,
attention deficit and hyperactivity disorder, and oppositional defiant disorder
(Wibbelink, Hoeve, Stams, & Oort, 2017).
Longitudinal studies have shown that antisocial behavior leads to mental health
problems and that antisocials' emotional problems develop simultaneously along
with the severity of their offense (Ttofi,
Piquero, Farrington, & McGee, 2019). Although the relationship among
the dynamics between mental health and delinquency is unclear, the evidence
shows that mental health treatments reduce the probability of recidivism in
adolescents (Robst, 2017). Teenagers
with drug use issues are more likely to relapse (Van der Put, Creemers, & Hoeve, 2014)
and less responsive to treatments (Cox et
al., 2018). There is also a growing interest in assessing the effect of
cognitive and emotional morality on recidivism, although the evidence in this
field is inconclusive (Ferguson & Wormith,
2013; Körner, Schindler, & Hahnemann, 2017; Van Vugt et al., 2011).
It has been recommended that antisocial behavior centers
evaluate interventions and apply efficient models (Andrews & Bonta, 2017; Drawbridge, Truong, Nguyen, Lorenti, &
Vincent, 2021). Similarly, resarch has highlight the need for professionals
serving adolescents to develop appropriate competencies to deliver effective
interventions and guarantee adolescents' rights (Vargas-Muñoz & Alarcón-Espinoza, 2021).
Current study
Adolescents who comply with judicial measures tend to have been
exposed to multiple risk factors that hinder their successful reentry into
society. Existing studies in Colombia that refer to recidivism have pointed out
that it is necessary to identify and intervene in antisocial behavior's
personal, family, and school factors (Molina
Sierra, 2018). It has also been suggested that the SRPA should be improved
in terms of human talent, pedagogical intervention, educational processes,
impact evaluation, data systematization, infrastructure, and financial
resources (Arias, 2015).
A literature review shows few specific studies on risk and
protective factors in recidivism among Colombian adolescents, and they are
limited to specific jurisdictions (Molina
Sierra, 2018). It is necessary to establish these factors to favor their intervention.
Given this need, this study aims to examine the influence of risk factors and
protective factors on recidivism.
The results may be useful in interventions with adolescents who
have already committed crimes, taking into consideration the factors of most
significant risk and developing prevention strategies in a context that
presents high vulnerability and risks of antisocial behavior for children and
adolescents.
Method
Participants
The participants were 646 adolescents between 14 and 19 years of
age (Mage = 17.08, SD = 1.23), and 15 % were girls. Of the
participants, 90.2 % were over sixteen years of age, and the remaining 9.8 %
were between fourteen and fifteen years of age. The age coincides with the peak
of antisocial behavior in adolescents that occurs between the ages of fifteen
and nineteen and then declines at twenty. Of these, 1.1 % were not studying,
8.8 % were in elementary school, 62.9 % were in high school, and 27.2 % in
secondary advanced school. In terms of school performance, 61.4 % reported that
they had repeated two or more school years, 25.3 % one school year, and 13.3 %
reported no repeated academic years. 17 % lived with parents and siblings; 34.4
% with close relatives, usually with grandparents; 27 % with one parent; 17.9 %
with one parent in a reconstituted family, and 3.2 % had been abandoned, given
up for adoption, or lived alone.
Considering the nature of the sample needed for this study, the
participants were chosen non-probabilistically and for convenience. Authorization
was requested from the ICBF for the
centers with the largest number of adolescents in treatment in each region to
ensure the largest possible number of participants. Those over 14 years of age
could participate, which coincides with the legal criteria in Colombia for
entering the SRPA (14-18 years); they had to be able to read and write
comprehensively, and not have a severe psychiatric disorders diagnosis.
All the adolescents belonged to centers of the SRPA in Colombia
in the Departments of Cundinamarca (n = 248, 38.4 %), Antioquia (n = 151, 23.4
%), Caldas (n = 123, 19 %), Cauca (n = 50,7.7 %), Boyacá (n = 36, 5.6 %)
Casanare (n = 16, 2.5 %) and Nariño (n = 22, 3.4 %). Based on the average
annual income of adolescents prosecuted and sanctioned (19.052 admissions to
the SRPA in 2017; ICBF, 2020), it was calculated that a sample of 407
adolescents was representative of the population (99 % confidence level with
loss adjustment). This indicates that the sample obtained is adequate.
Procedure
First, the Monitoring and Evaluation Subdirectorate, the
Planning and Management Control Division, and the Headquarters of the General
Directorate (Instituto Colombiano de Bienestar Familiar) issued the study's
authorizations (SIM E-2016-660327-0111). The data was collected according to
the standards of the Declaration of Helsinki (World Medical Association, 2013). The
study was conducted according to the ethical principles established by the APA
and by the Deodontological and Bioethical Code of the Colombian Psychological
Association. A confidentiality commitment was signed, which established the
conditions concerning data use and the responsibilities acquired with the investigation.
Secondly, informed consent was obtained from all the judicial
authorities representing adolescents, defenders, and family workers. The legal
representatives, parents, and each of the participants included in the study
also signed a consent form. Participation was entirely voluntary, and
withdrawal was possible at any time. It did not imply any serious risk for the
participants, and the participants were informed from the beginning that
participation would entail no financial compensation.
Third, the application was arranged with the directors of the
centers and was carried out in paper format, individually or in small groups of
five or fewer participants and was applied by the research team.
Fourth, to describe the sociodemographic characteristics and
offenses and identify repeat offenders, the participants' court records were
consulted and verified with the centers' professionals working with the
adolescents. Likewise, verifications were made to confirm that the files' data
had the same information reported by the participants. This study assessed
recidivism retrospectively; that is, we evaluated recidivism when it had
already occurred. Although longitudinal assessments and follow-up cohorts are
recommended, retrospective recidivism assessment methods have been useful to
elaborate antisocial trajectories from childhood to adulthood (Valdivia-Devia, Oyanedel, & Andrés-Pueyo,
2018) and to detect risk and protective factors (Ortega-Campos, García-García, De La
Fuente-Sánchez, & Zaldívar-Basurto, 2020).
Instruments and measures
The Communities That Care Youth Survey (CTC-YS; Arthur, Hawkins, Pollard, Catalano, &
Baglioni, 2002) was used to assess risk and protective factors. This is a
135-item instrument designed to measure a broad set of risk and protective
factors identified across the community, school, family, peer group, and
individual domains, as well as behavioral outcomes, including drug use,
violence, antisocial behavior, and crime (Rhew
et al., 2016). The CTC-YS is appropriate for adolescents, and the
questionnaire is mixed (polychotomous and dichotomous) in its form and takes 40
minutes to complete. The CTC has been widley used in Colombian adolescents with
chronbach alphas ranging from .60 to .96 in all factors assessed by the
questionnaire (Trujillo, Obando, &
Trujillo, 2016; Trujillo, Obando,
& Trujillo, 2019).
Recidivism
Evaluated as official criminal participation (based on a legal
record). After being convicted of a previous offense, the teenager committed a
new offense, which incurred a new conviction. The variable was coded
dichotomously to differentiate between recidivists (1) and non-recidivists (0).
This criterion is the most frequently used in the evaluation of this construct
(Mallett et al., 2013; Robst, 2017; Van der Put et al., 2014).
Antisocial behavior
The CTC-YS scale was used to analyze the association between the
study variables and antisocial behavior. The items in this factor refer to
criminal and antisocial behavior. The items include: how many times have you
carried guns? How many times have you sold illegal drugs? How many times have
you been arrested? How many times have you purposely damaged or destroyed
property that did not belong to you, and how many times have you attacked
someone with the idea of seriously hurting them? The items are presented on an
8-point scale that is scored ranging from never (1) to 40 times or more (8).
Risk factors and protective factors
CTS-YS Community factors assess community's conditions, the
structures, and attitudes of its members. By way of examples, the perceived
availability of drugs factor asks: if you wanted to get some beer, wine, or
liquor (for example, vodka or whisky), how difficult would it be to get it in
your neighborhood? If you wanted to get a drug like cocaine, LSD, ecstasy, or
amphetamines, how difficult would it be to get it in your neighborhood? If you
wanted to get some marijuana, how difficult would it be to get it in your
neighborhood? The response options are very hard (1), quite hard (2), quite
easy (3) and very easy (4).
Family factors measure the conditions, attitudes, and behaviors
in the family that affect its members' present and future development. As an
example, the poor family management factor includes the following items: (1) in
my family the rules are clear; (2) my parents ask me if I have finished my
homework; (3) when I am not at home, one of my parents knows where I am and who
I am with; (4) my family has clear rules about the use of alcohol and drugs;
(5) if you drank beer, wine, or spirits (for example, vodka or whiskey) without
your parents' permission, would they find out?; (6) If you skipped school,
would your parents notice? The response options are: NO!
(4), no (3), yes (2), YES! (1).
School factors evaluate events related to problematic behaviors
at school and educational socialization scenarios. For example, the
opportunities for school prosocial involvement factor is evaluated using
questions like: in my school, students have many opportunities to help decide
things like class activities and rules; at my school, students are offered many
opportunities to speak to teachers one to one; I am offered many opportunities
to participate in class discussions or activities. The answer options are: NO! (1), no (2), yes (3), YES! (4).
Individual and peer group factors measure personal
characteristics and attitudes that are inherent in the person and that may in
some cases be influenced, by close relationships with peers. For example, the
early initiation of antisocial behavior factor is evaluated with the following
items: How old were you when you first … got suspended from school? …got
arrested? …carried a handgun? …attacked someone with the idea of seriously
hurting them? The response options are: 10 or younger
(8), 11 years (7), up to 17 or
older (1), never have (0).
Data analysis
The data analysis was carried out using the SPSS V. 25.00
statistical software package. To obtain the risk factors and protective
factors, the responses were coded according to the instructions of the Youth
Survey Scale Dictionary (Social
Development Research Group, 2014). The descriptive results include the
mean, standard deviation of each factor and reliability (Table
1). After descriptive analyzes, Pearson's bivariate correlations were
performed to examine the relationship between all the study factors and the
antisocial behaviour variable. Finally, a logistic binary regression model was
estimated to test the effect of the study variables on recidivism.
Logistic regression is used to predict the outcome of a
categorical variable as a function of the predictor variables; it assesses the
probability of an event (recidivism) occurring as a function of other variables
(risk and protective factors). To be performed, some conditions must be
guaranteed: the multicollinearity tests and error independence tests, which
were verified using a multiple linear regression (Hilbe, 2009). The Durbin-Watson test
indicated compliance with the error independence assumption (1.533) (King & Harris, 1995) and the
Inflation Variance Factor (IVF) (1.113-2.911) values indicated low
multicollinearity between the variables (García,
García, López, & Salmerón, 2015). Logistic binary regression was
selected for prediction, as it is one of the most widely used procedures in the
study of recidivism (Cox et al., 2018;
Mallett et al., 2013; Nally et al., 2014;Robst, 2017; van der Put & De Ruiter, 2016). In
this case, the fit of the regression model was analyzed with the
Hosmer-Lemeshow goodness-of-fit test, and a significance greater than .05 was
observed X. = 7.258, [gl
= 8], . = .509) with good model fit (Hosmer,
Lemeshow & Sturdivant, 2013).
Results
Offenses and drugs among the participants
Amongst the offenses for which these adolescents were
judicialized are: crimes against property, simple or aggravated theft (36.4 %),
trafficking, possession, and manufacturing of drugs (20.7 %), homicide (7.9 %),
personal injury (8.4 %), manufacturing, traffic and carrying firearms and
ammunition (5.0 %), sexual crimes (5.1 %), damage to the property of others
(4.5 %), domestic violence (4.0 %), street life and associated conduct (1.9 %),
membership of armed groups (1.2 %), attempted murder (1.4 %), extortion (1.1
%), conspiracy to commit a crime (0.8 %), receiving (0.5 %), assault on a
public servant (0.6 %), kidnapping (0.2 %) and, membership of a criminal
organization (0.2 %). The adolescents' distribution of crimes in the study
coincides with the statistics reported by official sources in Colombia (ICBF,
2020). 77.57 % of the prosecutions of adolescents are grouped into four
categories: misdemeanors against property, theft, and robbery (36.32 %),
trafficking, possession, and manufacturing of narcotics (28.26 %), personal
assaults (8.51 %), and manufacturing and carrying arms (5.93 %).
The participants reported high levels of drug use, including
frequent consumption of cigarettes (81.5 %), alcohol (91.2 %), marijuana (83.9
%), LSD (50 %), cocaine (42.3 %), ecstasy (33.7 %), amphetamines (25.1 %),
over-the-counter medications (25.2 %), coca paste base, or basuco (33.7 %) and other illegal drugs (55.7 %).
Risk and protection factors associated with antisocial
behavior.
The relationship between the study variables and antisocial
behavior was examined using Pearson correlation coefficients. Risk factors
positively correlated with antisocial behavior, and protective factors were
negatively associated with antisocial behavior (Table 1).
It is observed that the variables most related to antisocial and
delinquent behavior are family history of antisocial behavior, parental
attitudes favorable to antisocial behavior, gang involvement, favorable
attitudes to antisocial behavior and drug use, interaction with antisocial
peers, and rewards for antisocial behavior involvement. The variables that are
negatively related to antisocial and delinquent behavior are belief in a moral
order, prosocial-individual involvement, and social skills.
Table 1
Mean, standard
deviation and reliability of risk and protective factors and association of
variables with antisocial behavior (n = 646)
Factors |
Rank |
M |
SD |
Cronbach's Alpha |
Association with
antisocial behavior |
Risk factors
Community risk factors |
|||||
Low neighborhood
attachment |
1-4 |
2.19 |
1.00 |
.76 |
-.135 ** |
Community
disorganization |
1-4 |
2.64 |
0.62 |
.62 |
-.330 ** |
Transition and
mobility |
1-5 |
2.28 |
0.72 |
.49 |
.151 ** |
Perceived availability
of drugs |
1-4 |
2.94 |
0.93 |
.83 |
.255 ** |
Perceived
availability of handguns |
1-4 |
2.16 |
1.11 |
N / A |
.343 ** |
Laws and norms
favorable to drug use |
1-4 |
2.51 |
0.62 |
.72 |
.244 ** |
Family risk factors |
|||||
Family history of
antisocial behavior |
1-5 |
2.75 |
1.05 |
.78 |
.337 ** |
Poor family
management |
1-4 |
2.23 |
0.60 |
.76 |
.175 ** |
Family conflict |
1-4 |
2.12 |
0.66 |
.53 |
.198 ** |
Parental attitudes
favorable to drug use |
1-4 |
1.68 |
0.75 |
.76 |
.279 ** |
Parental attitudes
favorable to antisocial behavior |
1-4 |
1.61 |
0.75 |
.80 |
.326 ** |
School risk factors |
|||||
Academic failure |
1-11 |
5.53 |
1.40 |
.64 |
-.30 |
Low commitment to
school |
1-4 |
2.29 |
0.53 |
.61 |
.205 ** |
Individual and peer
group risk factors |
|||||
Rebelliousness |
1-4 |
2.38 |
0.69 |
.53 |
.278 ** |
Gang involvement |
1-4 |
2.30 |
2.56 |
.84 |
.389
** |
Perceived risk of
drug use |
1-4 |
2.10 |
0.86 |
.81 |
.097 * |
Early initiation in
drug use |
1-8 |
4.36 |
2.07 |
.85 |
.326 ** |
Early initiation of
antisocial behavior |
1-8 |
3.35 |
1.62 |
.70 |
.325 ** |
Favorable attitudes
to drug use |
1-4 |
2.18 |
0.92 |
.84 |
.346 ** |
Favorable attitudes
to antisocial behaviour |
1-4 |
1.98 |
0.82 |
.86 |
.378
** |
Seeking of
sensations |
1-4 |
3.34 |
1.38 |
.72 |
.414 ** |
Rewards for
antisocial behavior involvement |
1-5 |
1.99 |
1.01 |
.77 |
.399
** |
Friends´ use of
drugs |
1-4 |
2.06 |
1.28 |
.79 |
.432 ** |
Interaction with
antisocial peers |
1-4 |
1.32 |
1.07 |
.85 |
.543 ** |
Intention to use
drugs |
1-4 |
3.02 |
0.82 |
.73 |
.238 ** |
Frequency of drug
use |
0-1 |
0.28 |
0.25 |
.89 |
.404 ** |
High frequency of
drug use |
0-1 |
0.28 |
.35 |
.70 |
.103 ** |
Protection factors |
|||||
Community protection
factors |
|||||
Opportunities for
prosocial involvement |
1-4 |
3.24 |
0.93 |
.64 |
-.032 |
Rewards for
prosocial involvement. |
1-4 |
2.38 |
0.78 |
.64 |
-.016 |
Family protection
factors |
|||||
Attachment in the
family |
1-4 |
2.60 |
0.78 |
.70 |
-.110 ** |
Opportunities for
prosocial family involvement |
1-4 |
2.86 |
0.82 |
.76 |
|
Rewards for
prosocial family involvement |
1-4 |
2.89 |
0.70 |
.66 |
-.119
** |
School protection
factors |
|||||
Opportunities for
prosocial school involvement |
1-4 |
2.72 |
1.01 |
.65 |
-.121 ** |
Rewards for
prosocial school involvement |
1-4 |
2.97 |
0.62 |
.71 |
-.116 ** |
Individual
protection factors |
|||||
Interaction with
prosocial peers |
1-4 |
1.82 |
1.01 |
.70 |
-.088 * |
Belief in a moral
order |
1-4 |
2.90 |
0.61 |
.57 |
-.342
** |
Prosocial-individual
involvement |
1-8 |
2.17 |
1.11 |
.68 |
-.197 ** |
Rewards for
prosocial-individual involvement |
1-5 |
2.63 |
1.04 |
.63 |
.171 |
Social skills |
1-4 |
2.56 |
0.76 |
.58 |
-.302 ** |
Religiosity |
1-4 |
2.60 |
1.02 |
N / A |
-.048 |
Antisocial behavior
variables |
|||||
Antisocial behavior
frequency |
0-1 |
0.45 |
0.31 |
.75 |
.813
** |
Logistic regression model
A logistic regression model was estimated to test the factors
with the most significant effect on recidivism, and gender and age were
included as control variables. The predictors were risk and protective factors,
and the dependent variable was recidivism. Non-recidivist adolescents were
identified with a score of zero (0), the recidivist adolescents whith a score
of one (1). In the sample, 32 % met the conditions for consideration as a
recidivist. The variables were included simultaneously, and the model was
significant (X2 = 161.3 [df=
43] . < .001, and correctly classified 74.6 % of the cases (Nagelkerke R. .31).
Overall, in the model evaluated, risk factors that explain the
probability of recidivism, i.e., those that were significant, showed a positive
beta coefficient (e. g., low commitment to school,
B = .337, p ≤ .05), and protective factors show negative betas (e. g., opportunities for prosocial school involvement B =
-.383, p ≤ .05). The relationship between the variables can be estimated using
the exp(b) statistic. Values greater than 1 indicate that an increase in the
independent variable is associated with a higher probability of recidivism;
conversely, values less than one indicate that an increase in the independent
variable is associated with a decrease in the probability of recidivism.
The logistic regression model results (Table 2)
indicate that risk factors increase the probability of recidivism and
protective factors decrease it. It is noteworthy that the rewards for prosocial
family involvement and religiosity were associated with a higher probability of
recidivism. These observations could indicate that in the participants of this
study, family reward dynamics could negatively reinforce the behavior and that
attending religious activities does not prevent antisocial activities.
Among the family factors, parental attitudes favorable toward
drug use and antisocial behavior indicate higher recidivism rates. Likewise,
low commitment to school, early initiation in drug use and interaction with
antisocial peers influence recidivism. Protective factors that reduce the
probability of recidivism are belief in a moral order, opportunities for
community prosocial involvement, and opportunities for prosocial school
involvement.
Table 2
Logistic binary
regression evaluating effects on recidivism (n = 646)
Variables |
B |
exp(b) [95 % CI] |
|||
Gender (boys) |
2.605
*** |
.479 |
29.541 |
13.525 [5.287,
34.598] |
|
Age |
-.303 *** |
.089 |
11.536 |
.739 [621, 880] |
|
Grade |
-.022 |
.047 |
.215 |
.979 [893, 1.072] |
|
Risk factors |
|||||
Community risk
factors |
|||||
Low neighborhood
attachment |
.048 |
.120 |
.158 |
1.049 [829, 1.326] |
|
Community
disorganization |
-.007 |
.126 |
.003 |
.993 [776, 1.271] |
|
Transition and
mobility |
.039 |
.103 |
.146 |
1.040 [851, 1.271] |
|
Perceived
availability of drugs |
.023 |
.129 |
.032 |
1.024 [795, 1.318] |
|
Perceived
availability of handguns |
-.012 |
.107 |
.012 |
.988 [801, 1.220] |
|
Laws and norms
favorable to drug use |
-.013 |
.172 |
.006 |
.987 [704, 1.384] |
|
Family risk factors |
|||||
Family history of
antisocial behaviour |
-.142 |
.126 |
1.263 |
.868 [678, 1.111] |
|
Poor family
management |
.009 |
.139 |
.004 |
.992 [755, .1032] |
|
Family conflict |
.073 |
.114 |
.412 |
1.076 [860, 1.346] |
|
Parental attitudes
favorable toward drug use |
.290
* |
.146 |
3.923 |
1.336 [1.003, 1.780] |
|
Parental attitudes
favorable to antisocial behavior |
.288
* |
.143 |
4.051 |
1.133 [1.008, 1.765] |
|
School risk factors |
|||||
Academic failure |
.100 |
.118 |
.715 |
1.105 [877, 1.392] |
|
Low commitment to
school |
.337 * |
.157 |
4.625 |
1.401 [1.030, 1.906] |
|
Individual and peer
group risk factors |
|||||
Rebelliousness |
.064 |
.122 |
.272 |
1.066 [839, 1.353] |
|
Gang involvement |
.014 |
.115 |
.014 |
1.014 [809, 1.271] |
|
Perceived risk of
drug use |
-.042 |
.113 |
.141 |
.958 [768, 1.196] |
|
Early initiation in
drug use |
.404** |
.138 |
8.548 |
1.498 [1.143, 1.946] |
|
Early initiation of
antisocial behavior. |
-.014 |
.125 |
.013 |
.986 [772, 1.259] |
|
Favorable attitudes
to drug use |
-.269 |
.170 |
2.500 |
.764 [548, 1.067] |
|
Favorable attitudes
toward antisocial behavior |
.274 |
.167 |
1.316 |
1.316 [949, 1.825] |
|
Seeking of
sensations |
.087 |
.120 |
.527 |
1.091 [862, 1.382] |
|
Rewards for
antisocial behavior involvement |
.104 |
.117 |
.779 |
1.109 [881, 1.396] |
|
Friends´ use of
drugs |
-.039 |
.146 |
.073 |
.961 [723, 1.279] |
|
Interaction with
antisocial peers |
.309 * |
.146 |
4.658 |
1.362 [1.029, 1.804] |
|
Intention to use
drugs |
-.167 |
.121 |
1.904 |
.846 [667, 1.073] |
|
Frequency of drug
use |
.034 |
.122 |
.078 |
1.035 [814, 1.315] |
|
High drug use
frequency |
-.234* |
.116 |
4.063 |
.791 [630, 994] |
|
Protection factors |
|||||
Community protection
factors |
|||||
Opportunities for
prosocial involvement |
-.207* |
.105 |
3.898 |
.813 [662, 998] |
|
Rewards for
prosocial involvement. |
.102 |
.122 |
.696 |
1.107 [871, 1.407] |
|
Family protection
factors |
|||||
Attachment in the
family |
-.270 |
.159 |
2.897 |
.763 [559, 1.042] |
|
Opportunities for
prosocial family involvement |
-.297 |
.170 |
3.054 |
.743 [533, 1.037] |
|
Rewards for
prosocial family involvement |
.385* |
.183 |
4.445 |
1.470 [1.027, 2.103] |
|
School protection
factors |
|||||
Opportunities for
prosocial school involvement |
-.383* |
.151 |
6.444 |
.682 [507, 916] |
|
Rewards for
prosocial school involvement |
-.240 * |
.110 |
4.784 |
.903 [711, 1.147] |
|
Individual
protection factors |
|||||
Interaction with
prosocial peers |
.094 |
.115 |
.673 |
1.099 [877, 1.376] |
|
Belief in a moral
order |
-.292* |
.140 |
4.336 |
.747 [567, 983] |
|
Prosocial-individual
involvement |
.069 |
.109 |
397 |
1.071 [865, 1.326] |
|
Rewards for
prosocial-individual involvement |
-.194 |
.115 |
2.844 |
.824 [658, 1.032] |
|
Social skills |
-.086 |
.123 |
.496 |
.917 [721, 1.167] |
|
Religiosity |
.239* |
.110 |
4.704 |
1.269 [1.023, 1.575] |
|
Constant |
3.905 |
.2444 |
2.554 |
49.635 |
|
Df |
(1) |
||||
Nagelkerke R2 |
.31 |
* p < .05** p < .01***
p < .001.B: Unstandardized coefficientsSD: standard deviationW: Wald test; exp(b) [95 % CI]
(confidence intervals)DF:
degree of freedom.
Discussion and
Conclusions
The aim of this study was to examine the influence of risk
factors and protective factors on recidivism in a sample of Colombian juvenile
offenders. In recidivism studies with adolescents, the aim is to identify the
risk and protective factors involved and determine the variables that favor
intervention and reduce the recidivism (Moffitt,
2018). It was possible to identify relationships between risk and
protective factors with antisocial behavior and provide evidence on the factors
that favor recidivism.
As criminological theories point out, antisocial behavior is
multicausal (Andrews & Bonta, 2017; Moffitt, 2018). Community, family,
school, and individual risk, and protective factors related to antisocial
behavior can affect each adolescent differently; this implies a major challenge
for the recidivism intervention that has to cover all these dimensions (Singh et al., 2018).
In this study, at the community level, we observed an
association between antisocial behavior with the perceived availability of
handguns and drugs, laws, and norms favorable to drug use, and community
disorganization. These aspects can be intervened by offering to the community:
social services, improved support networks, safe environments, and ensuring
access to leisure activities for adolescents (Cuervo-Gómez et al., 2017; Schubert et al., 2016). In addition, it
is necessary to offer support programs in the community for adolescents leaving
the penal system to encourage desistance (Chamberlain,
Boggess, & Powers, 2016;Schubert,
Mulvey, & Pitzer, 2016).
At the family level, the study's findings indicate that the
probability of recidivism increases when there are favorable parental attitudes
toward drug use and antisocial behavior. It was also detected that the
behavioral rewards offered by the family might be a factor that increases the
probability of recidivism. The social development model on which the instrument
used for assessment in this study indicates that inconsistency in sanctioning
undesirable behaviors and rewarding negative behaviors increases the risk of
drug use, violence, and delinquency (Arthur,
Hawkins, Pollard, Catalano, & Baglioni, 2002). These family conditions
of Colombian adolescents in the penal system should be analyzed and intervened
because they reflect other crises in the immediate environment that may affect
the intervention and social reintegration processes. As observed in other
studies, a lack of family support could favor antisocial dynamics or hinder
reentry processes (Pleggenkuhle et al.,
2016).
In the school setting, it is observed that low commitment and
lack of school opportunities increase the probability of recidivism.
Adolescents report a high level of failure, and it is inferred that there is no
correspondence between the performance of adolescents in the judicial centers
and those that their peers of a similar age achieve in the regular school
system. Previous findings have suggested that structuring robust academic
interventions, particularly in reading, can effectively reduce rates of
antisocial behavior and recidivism (Katsiyannis,
Ryan, Zhang, & Spann, 2008). Inclusion in the school system, quality
education, the creation of educational proposals adapted to the needs of
adolescents, and educational continuity may be alternatives that help SRPA
adolescents in Colombia develop their life projects (Martinez Virto, 2021; Silver, Cochran, Motz, & Nedelec, 2020).
At the individual level, it is suggested that in each
adolescent, the subject's risk factors are identified, intervention needs are
determined, and the subject's response possibilities and institutional and
contextual resources are evaluated and implemented (Andrews & Bonta, 2017).
In this study it was observed that relationships with antisocial
peers increase the likelihood of recidivism. Association with antisocial peers
is a recognized risk factor for antisocial behavior and recidivism (Boman & Mowen, 2017; Spruit et al., 2017). This is a central
factor in both interventions for criminal behavior and the prevention and
control of recidivism (Andrews & Bonta,
2017). The evidence suggests that the impact of peers on antisocial
behavior decreases with age, because people achieve greater resistance,
identity, and independence, and as such desistance may be linked to normative
changes in relationships with peers that occur as individuals mature socially
and emotionally (Monahan et al., 2009).
It is notable that in this study, belief in a moral order was a
protective factor that decreases the probability of recidivism. The content of
this factor in the CTC-YS refers to the importance of telling the truth, even
if this leads to punishment; judgment on the rights and wrongs of starting
conflicts and fights; discernment about aggressive responses; opinions about
taking other people’s belongings; and being dishonest in one’s tasks and
responsibilities (Social Development
Research Group, 2014). The results indicate that belief in a moral order is
a factor that should focus on intervention, and it’s important to examine their
influence in the desistance in juvenile offenders. Consistent with our
observation, it has been suggested that moral development and moral emotions
might affect recidivism (Körner,
Schindler, & Hahnemann, 2017; Van
Vugt et al., 2011). Contrary to what was observed in belief in a moral
order, religiosity increased the probability of recidivism. This observation
could be explained by how the variable was assessed since only attendance at
religious activities is asked.
Antisocial behavior and recidivism are complex, and risk and
protective factors are not isolated conditions. In Colombia, several contextual
factors favor antisocial behavior and hinder the social reintegration of SRPA
adolescents. It is unclear whether offenses are related to social exclusion
conditions, poverty, and limited access to education. However, the poverty
experienced by judicialized adolescents in Colombia is relevant. 93 % of these
adolescents have limited socioeconomic resources. Only 23 % have completed
elementary education, 24 % primary education, and only 5 % are high school
graduates, while entry to university is practically non-existent in this
population (Ministerio de Justicia y del
Derecho, 2014).
Another notable problem among SRPA adolescents is drug use.
Considering the high rate of drug use reported by the participants,
establishing strategies and intervention programs to address this problem could
help mitigate the effect on recidivism. Above all, it is important to prevent
early initiation of drug use, which has been observed to influence recidivism.
The evidence available suggests that adolescents with drug use issues are more
likely to relapse (Van der Put et al.,
2014), show greater resistance to change, and respond to treatments to a
lesser extent (Cox et al., 2018).
Although this study achieves its objective and provides
important data for understanding the risk and protective factors in recidivism
among judicialized adolescents in Colombia, it has several limitations,
including the fact that it did not cover the entire spectrum of recidivism
variables, and personality traits, mental disorders, psychopathy, and medical
conditions were not included. Analyses differentiated by gender are limited due
to the limited proportion of girls, and establishing these differences is
increasingly important (Moffitt, 2018).
No analysis by type of offense was carried out, and this information is
important for understanding the factors that motivate and maintain the
commission of specific infraction.
Due to the methodology employed, this study should be considered
exploratory and applied only to the research context; future research can
contrast the data. We consider that the instrument used is suitable for the
population and has the advantage of including a wide range of factors; however,
it is necessary to continue evaluating its psychometric properties.
In general, recidivism studies have recognized limitations,
including the fact that they are carried out with data reported by official bodies
and are based on legal criteria, which conceals criminal acts that are not
prosecuted. As regards to the veracity of the risk and protective factors
evaluated, self-reports may present biases such as social desirability.
Future research may address risk and protective factors focusing
on the population with desistance and explain the community, family, school,
and individual aspects that favor antisocial behavior abandonment. Studies
could also focus on identifying school factors and the individual conditions of
recidivists related to poor performance and low school commitment levels.
Family typologies and the family's internal dynamics is a subject that requires
further investigation, not only during the process of imprisonment and
compliance with legal sentences but also during the process of reentering the
social and family context. In Colombia, longitudinal studies should be carried
out to determine the factors associated with recidivism and desistance, as has
been achieved in other contexts (Lee et
al., 2020; Zara & Farrington,
2016).
Finally, it is important to suggest that the State must
guarantee resources and establish policies and mechanisms to ensure adequate
care for adolescents in the SRPA, guaranteeing their constitutional rights
during the intervention and in the reentry process. Center operators are
responsible for implementing, monitoring, and evaluating the effectiveness of
intervention programs with adolescents. In this regard, they have indicated
that intervention models that help reduce recidivism should be implemented and
evaluated (Andrews & Bonta, 2017).
Likewise, it is necessary for professionals who serve adolescents to develop
the appropriate competencies to carry out effective interventions (Vargas-Muñoz & Alarcón-Espinoza, 2021).
References
Andrews, D. A.,
& Bonta, J. (2017). The psychology of criminal conduct
(6th Ed.). London, England: Routledge.
Arias, J. O. V.
(2015). La resocialización y la reincidencia de adolescentes en conductas
delictivas en el Departamento de Caldas, Colombia. Summa
Iuris, 3(2), 377–390. https://www.funlam.edu.co/revistas/index.php/summaiuris/article/view/1834/1464
Arthur, M. W.,
Hawkins, J. D., Pollard, J. A., Catalano, R. F., & Baglioni, A. J. (2002).
Measuring risk and protective factors for substance use, delinquency, and other
adolescent problem behaviors: The communities that care youth survey. Evaluation Review, 26(6), 575–601. https://doi.org/10.1177/0193841X0202600601
Baglivio, M. T.,
Jackowski, K., Greenwald, M. A., & Howell, J. C. (2014). Serious, violent,
and chronic juvenile offenders: A statewide analysis of prevalence and
prediction of subsequent recidivism using risk and protective factors. Criminology and Public Policy, 13(1), 83–116. https://doi.org/10.1111/1745-9133.12064
Boman, J. H., &
Mowen, T. J. (2017). Building the ties that bind, breaking the ties that don’t:
Family support, criminal peers, and reentry success. Criminology
and Public Policy, 16(3), 753–774. https://doi.org/10.1111/1745-9133.12307
Cacho, R.,
Fernández-Montalvo, J., López-Goñi, J. J., Arteaga, A., & Haro, B. (2020).
Psychosocial and personality characteristics of juvenile offenders in a
detention centre regarding recidivism risk. European
Journal of Psychology Applied to Legal Context, 12(2), 69–75. https://doi.org/10.5093/EJPALC2020A9
Chamberlain, A. W.,
Boggess, L. N., & Powers, R. A. (2016). The impact of the spatial mismatch
between parolee and employment locations on recidivism. Journal
of Crime and Justice, 39(3), 398–420. https://doi.org/10.1080/0735648X.2014.965264
Chu, C. M., Daffern,
M., Thomas, S., & Lim, J. Y. (2012). Violence risk and gang affiliation in
youth offenders: A recidivism study. Psychology, Crime and
Law, 18(3), 299–315. https://doi.org/10.1080/1068316X.2010.481626
Coupland, R. B. A.,
& Olver, M. E. (2020). Assessing protective factors in treated violent
offenders: Associations with recidivism reduction and positive community
outcomes. Psychological Assessment, 32(5), 493–508.
https://doi.org/10.1037/pas0000807
Cox, S. M., Kochol,
P., & Hedlund, J. (2018). The exploration of risk and protective score
differences across juvenile offending career types and their effects on
recidivism. Youth Violence and Juvenile Justice, 16,
77–96. https://doi.org/10.1177/1541204016678439
Cuervo-Gómez, K.,
Villanueva-Badenes, L., & Pérez-Castillo, J. M. (2017). Riesgo de
reincidencia y evolución, a través del Inventario IGI-J en una población de
menores infractores. Revista Internacional De Sociologia,
75(2), e065. https://doi.org/10.3989/ris.2017.75.2.15.94
Cuevas, C., Wolff,
K. T., & Baglivio, M. T. (2019). Dynamic risk factors and timing of
recidivism for youth in residential placement. Journal of
Criminal Justice, 60, 154–166. https://doi.org/10.1016/j.jcrimjus.2018.10.003
De Vries Robbé, M.,
de Vogel, V., Douglas, K. S., & Nijman, H. L. I. (2015). Changes in dynamic
risk and protective factors for violence during inpatient forensic psychiatric
treatment: Predicting reductions in postdischarge community recidivism. Law and Human Behavior, 39(1), 53–61. https://doi.org/10.1037/lhb0000089
Drawbridge, D. C.,
Truong, D., Nguyen, N. T., Lorenti, V. L., & Vincent, G. M. (2021).
Risk–need–responsivity: Evaluating need-to-service matching with reach,
effectiveness, adoption, implementation, maintenance. Behavioral
Sciences and the Law, 39(1), 106–122. https://doi.org/10.1002/bsl.2502
Ferguson, L. M.,
& Wormith, J. S. (2013). A meta-analysis of moral reconation therapy. International Journal of Offender Therapy and Comparative
Criminology, 57(9), 1076–1106. https://doi.org/10.1177/0306624X12447771
García, C. B.,
García, J., López, M. M., & Salmerón, R. (2015). Collinearity: revisiting
the variance inflation factor in ridge regression. Journal
of Applied Statistics, 42(3), 648–661. https://doi.org/10.1080/02664763.2014.980789
García-García, J.,
Ortega-Campos, E., & de la Fuente-Sánchez, L. (2010). Juvenile offenders’
recidivism in Spain: A quantitative revision. In M. Frías-Armenta & V.
Corral-Verdugo (Eds.), Bio-psycho-social perspectives on
interpersonal violence (pp. 333–353). New York: Nova Science Publishers,
Inc.
Grossi, L. M.,
Brereton, A., Lee, A. F., Schuler, A., & Prentky, R. A. (2017). Sexual
reoffense trajectories with youths in the child welfare system. Child Abuse and Neglect, 68, 81–95. https://doi.org/10.1016/j.chiabu.2017.03.024
Hilbe, J. M. (2009).
Logistic regression models. Boca Raton: CRC Press.
Hosmer, D. W.,
Lemeshow, S., & Sturdivant, R. X. (2013). Applied
logistic regression (3rd Ed.). Hoboken, New Jersey: Jhon Wiley &
Sons. Inc.
Huebner, B. M.,
& Pleggenkuhle, B. (2015). Residential location, household composition, and
recidivism: An analysis by gender. Justice Quarterly, 32(5),
818–844. https://doi.org/10.1080/07418825.2013.827231
Instituto Colombiano
de Bienestar Familiar (ICBF) (2020). Estadística Sistema
de Responsabilidad Penal para Adolescentes. Instituto Colombiano de
Bienestar Familiar. https://www.icbf.gov.co/bienestar/observatorio-bienestar-ninez/tablero-srpa
Joo, H. J., &
Jo, Y. (2015). Family, school, peers, and recidivism among south korean
juvenile offenders: An event history analysis. Asian
Journal of Criminology, 10, 99–116. https://doi.org/10.1007/s11417-015-9205-2
Jutengren, G., &
Medin, E. (2019). Cross-ethnic friendship and prosocial behavior’s potential
significance to elementary children’s academic competence. Journal of Educational Research, 112(1), 38–45. https://doi.org/10.1080/00220671.2018.1431872
Katsiyannis, A.,
Ryan, J., Zhang, D., & Spann, A. (2008). Juvenile delinquency and
recidivism: The impact of academic achievement. Reading
and Writing Quarterly, 24(2), 177–196. https://doi.org/10.1080/10573560701808460
Kennedy, T. D.,
Edmonds, W. A., Millen, D. H., & Detullio, D. (2019). Chronic juvenile
offenders: Exploring risk factor models of recidivism. Youth
Violence and Juvenile Justice, 17(2), 174–193. https://doi.org/10.1177/1541204018770517
Khachatryan, N.,
Heide, K. M., & Hummel, E. V. (2018). Recidivism patterns among two types
of juvenile homicide offenders: A 30-year follow-up study. International Journal of Offender Therapy and Comparative
Criminology, 62(2), 404–426. https://doi.org/10.1177/0306624X16657052
King, M. L., &
Harris, D. C. (1995). The application of the Durbin-Watson test to the dynamic
regression model under normal and non-normal errors. Econometric
Reviews, 14(4), 487–510. https://doi.org/10.1080/07474939508800333
Körner, A., Schindler,
R., & Hahnemann, T. (2017). How moral emotions affect the probability of
relapse. In G. Pär Anders, R. Bull, A. Shaboltas, & E. Dozortseva (Eds.), Psychology and law in Europe: When West meets East (pp.
167–188). CRC Press.
Lee, W., Moon, J.,
& Garcia, V. (2020). The pathways to desistance: A longitudinal study of
juvenile delinquency. Deviant Behavior, 41(1),
87–102. https://doi.org/10.1080/01639625.2018.1519138
Mallett, C. A.,
Fukushima, M., Stoddard-Dare, P., & Quinn, L. (2013). Factors related to
recidivism for youthful offenders. Criminal Justice
Studies, 26(1), 84–98. https://doi.org/10.1080/1478601X.2012.705539
Martinez Virto, L.
(2021). Perfilado psicosocial para la intervención socioeducativa en los
servicios sociales. Interdisciplinaria, 38(2),
117–133. https://doi.org/10.16888/interd.2021.38.2.8
Ministerio de
Justicia y del Derecho. (2014). Recomendaciones para la garantia de derechos de
los adolescentes en conflicto con la ley en la formulación de los planes de
desarrollo de alcaldes y gobernadores 2016-2019. Ministerio
de Justicia. http://www.politicacriminal.gov.co/Portals/0/Documentos
SRPA/Documento Recomendaciones SRPA para Incidencia en PDT
Moffitt, T. E.
(2018). Male antisocial behaviour in adolescence and beyond. Nature Human Behaviour, 2, 177–186. https://doi.org/10.1038/s41562-018-0309-4
Molina Sierra, G.
(2018). Causas de reincidencia en los delitos de los menores en el SRPA en la
ciudad de Cartagena entre los años 2012 y 2015. Revista
Jurídica Mario Alario D’Filippo, 10(19), 126–155. https://dialnet.unirioja.es/descarga/articulo/6857115.pdf
Monahan, K. C.,
Steinberg, L., & Cauffman, E. (2009). Affiliation with antisocial peers,
susceptibility to peer influence, and antisocial behavior during the transition
to adulthood. Developmental Psychology, 45(6),
1520–1530. https://doi.org/10.1037/a0017417
Mowen, T. J., &
Boman, J. H. (2019). Do we have it all wrong? The protective roles of peers and
criminogenic risks from family during prison reentry. Crime
and Delinquency, 65(5), 681–704. https://doi.org/10.1177/0011128718800286
Nally, J. M.,
Lockwood, S., Ho, T., & Knutson, K. (2014). Post-release recidivism and
employment among different types of released offenders: A 5-year follow-up
study in the United States. International Journal of
Criminal Justice Sciences, 9(1), 16–34. https://www.semanticscholar.org/paper/Post-Release-Recidivism-and-Employment-among-Types-Nally-Lockwood/d7311771e99ea02417ca97fc3d9b8891e87b7347
Navarro-Pérez, J.
J., Viera, M., Calero, J., & Tomás, J. M. (2020). Factors in assessing
recidivism risk in young offenders. Sustainability, 12(3),
1111. https://doi.org/10.3390/su12031111
Ngo, F. T.,
Paternoster, R., Curran, J., & Mackenzie, D. L. (2011). Role-taking and
recidivism: A test of differential social control theory. Justice
Quarterly, 28(5), 667–697. https://doi.org/10.1080/07418825.2010.528013
Orlando, M. S.,
& Farrington, D. P. (2021). Risk Factors for Juvenile Recidivists Versus
One-Time Offenders in Argentina: Comparisons with Other Countries. International Criminology, 0123456789. https://doi.org/10.1007/s43576-021-00021-2
Ortega-Campos, E.,
García-García, J., De la Fuente Sánchez, L., & Zaldívar Basurto, F. (2012).
Metaanálisis de la reincidencia de la conducta antisocial penada en
adolescentes españoles. EduPsykhé: Revista de Psicología y
Educación, 11(2), 171–189. https://journals.ucjc.edu/EDU/article/view/3864
Ortega-Campos, E.,
García-García, J., De La Fuente-Sánchez, L., & Zaldívar-Basurto, F. (2020).
Predicting risk of recidivism in Spanish young offenders: Comparative analysis
of the SAVRY and YLS/CMI. Psicothema, 32(2),
221–228. https://doi.org/10.7334/psicothema2019.275
Piquero, A. R.,
Farrington, D. P., Nagin, D. S., & Moffitt, T. E. (2010). Trajectories of
offending and their relation to life failure in late middle age: Findings from
the Cambridge study in delinquent development. Journal of
Research in Crime and Delinquency, 47(2), 151–173. https://doi.org/10.1177/0022427809357713
Pleggenkuhle, B.,
Huebner, B. M., & Kras, K. R. (2016). Solid start: supportive housing,
social support, and reentry transitions. Journal of Crime
and Justice, 39(3), 380–397. https://doi.org/10.1080/0735648X.2015.1047465
Rhew, I. C., David
Hawkins, J., Murray, D. M., Fagan, A. A., Oesterle, S., Abbott, R. D., &
Catalano, R. F. (2016). Evaluation of community-level effects of communities
that care on adolescent drug use and delinquency using a repeated
cross-sectional design. Prevention Science, 17,
177–187. https://doi.org/10.1007/s11121-015-0613-4
Robst, J. (2017).
Disposition of charges, out-of-home mental health treatment, and juvenile
justice recidivism. International Journal of Offender
Therapy and Comparative Criminology, 61(11), 1195–1209. https://doi.org/10.1177/0306624X15615383
Schubert, C. A.,
Mulvey, E. P., & Pitzer, L. (2016). Differentiating serious adolescent
offenders who exit the justice system from those who do not. Criminology, 54(1), 56–85. https://doi.org/10.1111/1745-9125.12098
Silver, I. A.,
Cochran, J. C., Motz, R. T., & Nedelec, J. L. (2020). Academic achievement
and the implications for prison program effectiveness and reentry. Criminal Justice and Behavior, 47(7), 848–866. https://doi.org/10.1177/0093854820919790
Singh, J. P.,
Kroner, D. G., Wormith, J. S., Desmarais, S. L., & Hamilton, Z. (Eds.).
(2018). Handbook of recidivism risk/ needs assessment
tools. Hoboken, New Jersey: Wiley & Sons.
Social Development
Research Group. (2014). Youth Survey Scale Dictionary.
https://www.blueprintsprograms.org/resources/AEC_Youth_School_Survey.pdf
Sousa, S., Cardoso,
J., & Cunha, P. (2019). Risk and protective factors in criminal recidivist
inmates. Annals of Medicine, 51(sup1), 184–184. https://doi.org/10.1080/07853890.2018.1562754
Spruit, A., van der
Put, C., Gubbels, J., & Bindels, A. (2017). Age differences in the
severity, impact and relative importance of dynamic risk factors for
recidivism. Journal of Criminal Justice, 50, 69–77.
https://doi.org/10.1016/j.jcrimjus.2017.04.006
Steele, J. L.,
Bozick, R., & Davis, L. M. (2016). Education for incarcerated juveniles: A
meta-analysis. Journal of Education for Students Placed at
Risk, 21(2), 65–89. https://doi.org/10.1080/10824669.2015.1133308
Stojkovic, S. (Ed.).
(2017). Prisioner reentry: Critical issues and policy
directions. Milwaukee, Wisconsin: Palgrave Macmillan.
Ttofi, M. M.,
Piquero, A. R., Farrington, D. P., & McGee, T. R. (2019). Editorial—mental
health and crime: Scientific advances and emerging issues from prospective
longitudinal studies. Journal of Criminal Justice, 62,
1–2. https://doi.org/10.1016/j.jcrimjus.2018.10.004
Trujillo, Á.,
Obando, D., & Trujillo, C. A. (2016). Family dynamics and alcohol and
marijuana use among adolescents: The mediating role of negative emotional
symptoms and sensation seeking. Addictive Behaviors, 62,
99–107. https://doi.org/10.1016/j.addbeh.2016.06.020
Trujillo, C. A.,
Obando, D., & Trujillo, A. (2019). An examination of the association
between early initiation of substance use and interrelated multilevel risk and
protective factors among adolescents. PLoS ONE, 14(12),
1–18. https://doi.org/10.1371/journal.pone.0225384
Valdivia-Devia, M.,
Oyanedel, J. C., & Andrés-Pueyo, A. (2018). Trayectoria y reincidencia
criminal. Revista Criminalidad, 60(3), 251–267.
Van der Put, C. E.,
Creemers, H. E., & Hoeve, M. (2014). Differences between juvenile offenders
with and without substance use problems in the prevalence and impact of risk
and protective factors for criminal recidivism. Drug and
Alcohol Dependence, 134, 267–274. https://doi.org/10.1016/j.drugalcdep.2013.10.012
van der Put, C. E.,
& De Ruiter, C. (2016). Child maltreatment victimization by type in
relation to criminal recidivism in juvenile offenders. BMC
Psychiatry, 16, 24. https://doi.org/10.1186/s12888-016-0731-y
Van Vugt, E., Gibbs,
J., Stams, G. J., Bijleveld, C., Hendriks, J., & Van Der Laan, P. (2011).
Moral development and recidivism: A meta-analysis. International
Journal of Offender Therapy and Comparative Criminology, 55(8),
1234–1250. https://doi.org/10.1177/0306624X11396441
Vargas-Muñoz, M.,
& Alarcón-Espinoza, M. (2021). Competencias especializadas de intervención
en desadaptación social adolescente. Interdisciplinaria
Revista de Psicología y Ciencias Afines, 38(3), 67–82. https://doi.org/10.16888/interd.2021.38.3.4
Vaughn, M. G.,
Salas-Wright, C. P., DeLisi, M., & Maynard, B. R. (2014). Violence and
Externalizing Behavior Among Youth in the United States: Is There a Severe 5%? Youth Violence and Juvenile Justice, 12(1), 3–21. https://doi.org/10.1177/1541204013478973
Voisin, D. R., Tan,
K., Tack, A. C., Wade, D., & DiClemente, R. (2012). Examining parental
monitoring as a pathway from community violence exposure to drug use, risky
sex, and recidivism among detained youth. Journal of
Social Service Research, 38(5), 699–711. https://doi.org/10.1080/01488376.2012.716020
Walters, G. D.
(2018). Sibling delinquency as a risk factor for future offending: An
exploratory analysis. Youth Violence and Juvenile Justice,
16(4), 343–357. https://doi.org/10.1177/1541204017713255
Wibbelink, C. J. M.,
Hoeve, M., Stams, G. J. J. M., & Oort, F. J. (2017). A meta-analysis of the
association between mental disorders and juvenile recidivism. Aggression and Violent Behavior, 33, 78–90. https://doi.org/10.1016/j.avb.2017.01.005
Wolff, K. T.,
Baglivio, M. T., & Piquero, A. R. (2017). The Relationship between adverse
childhood experiences and recidivism in a sample of juvenile offenders in
community-based treatment. International Journal of
Offender Therapy and Comparative Criminology, 61(11), 1210–1242. https://doi.org/10.1177/0306624X15613992
World Medical
Association, W. (2013). World Medical Association Declaration of Helsinki:
ethical principles for medical research involving human subjects. Journal of American Medical Association (JAMA), 310,
2191–2194. https://doi.org/oi:10.1001/jama.2013.280740
Zara, G., & Farrington,
D. P. (2016). Criminal recidivism: explanation, prediction
and prevention. New York, NY: Routledge.
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