“Risk factors” are characteristics of the youth or his/her surroundings that increase the likelihood of negative outcomes, while “protective factors” are those that mitigate the impact of risk on positive youth outcomes.1,2 There is evidence that various types of risk and protective factors may “moderate” (i.e., influence) the effectiveness of mentoring programs.3
Drawing on Rhodes’4,5 theory of how youth mentoring achieves its impacts, DuBois et al.3 identified categories of potential moderators of the effectiveness of mentoring programs (i.e., factors that may increase or decrease the observed impact of mentoring program participation on outcomes). These include: youth interpersonal history (e.g., parental maltreatment, delinquency); social competencies (e.g., interpersonal sensitivity, capacity for engaging others); youth’s age; and family and community context (e.g., family relationships, school climate, neighborhood risk). While the evidence base, with respect to moderator effects, is clearer for some factors than others, there is broad consensus (and empirical evidence) that individual and environmental risk variables interact to influence the overall impacts of mentoring programs.3
Several types of risk and protective factors were selected for inclusion in the Toolkit. In most cases, these factors are not expected to be outcomes of mentoring programs, but rather, factors that could be measured prior to mentoring and tested as moderators of program benefits. The risk and protective factors considered thus far for the Toolkit include two family and community context factors (i.e., family management, neighborhood risk), three interpersonal history factors (i.e., deviant peer affiliation, peer victimization, and trauma exposure), factors that may increase susceptibility for opioid misuse as well as being impacted negatively by others' opioid misuse, and out-of-school-time (OST) structured activity involvement as a protective influence.i By incorporating assessments of these various risk and protective factors in their evaluations, programs may be able to gain useful information about factors that are most likely to influence the effectiveness of their programs. It is also possible that some of these risk and protective factors may be useful to assess as outcomes themselves, depending on program characteristics and aims (e.g., trauma symptoms).
Measures within this domain:
Family risk, including family structure (e.g., single-parent household), resources (e.g., socioeconomic status), and relationships (e.g., family conflict/dysfunction, parent-child relationship quality) have been noted as important potential influences on mentoring program effectiveness.3,4,5,6 Because many family context variables may influence the effectiveness of mentoring programs, selecting which measures to use is challenging. To represent family risk, we selected a measure of family management which taps into unclear expectations for behavior, monitoring of behavior, and inconsistent parenting practices (e.g., rewards and punishment). Limit setting and parental monitoring have been linked with other factors that are also hypothesized to moderate program effectiveness (e.g., youth problem behaviors, social competencies) as well as with negative behavioral outcomes such as drug use, violence, and delinquency.1
A lack of neighborhood resources and other neighborhood risk factors (e.g., crime, drug use, and or violence) have also been implicated as possible moderators of mentoring program effectiveness.3,4,5,6 Interest in the potential moderating role of such factors appears to stem, in part, from their well-documented contributions to negative youth outcomes. For example, physical deterioration of the neighborhood and high crime rates are associated with higher rates of youth problem behaviors such as juvenile crime and drug use, outcomes which themselves are potential moderators of intervention effectiveness.1 In this initial iteration of the Toolkit, a measure of community disorganization was selected that taps into physical deterioration, crime, and drug sales.
Deviant peer affiliation.
Deviant peer affiliation (DPA), or youths’ involvement with deviant peers, has long been identified as a cause of adjustment problems that include adolescent drug use, high-risk sexual behavior, and violent offenses.7,8,9,10,11 DPA may promote increases in deviant behavior through social modeling or peer pressure and various types of reinforcement that in turn reinforce the youth’s affiliation with deviant peers.12 DPA is also associated with several other aspects of the youth’s interpersonal history that may influence mentoring program effectiveness such as gang/involvement, delinquency, social competence, and peer rejection.3
Peer victimization refers to the experience of being targeted by hurtful teasing and aggressive behavior (e.g., experiencing bullying13). DuBois et al.3 and Rhodes4,5 identified peer rejection as a potential moderator of mentoring-program effectiveness. DuBois et al.3 note that youth who have experienced peer rejection may enter mentoring relationships with heightened interpersonal sensitivity14 and this may in turn affect the developing relationship and its ability to benefit youth (see also Kanchewa, Yoviene, Schwartz, Herrera, & Rhodes15). As such, peer victimization may be particularly important to assess as a moderator of program effects.
OST structured activity involvement.
Out-of-school-time (OST) structured activity involvement is characterized by youth involvement in organized activities such as sports and other afterschool and community-based programs or clubs. OST involvement is related to positive youth outcomes such as social competence and may be a protective factor against problem behaviors including gang involvement and delinquency.16,17 Thus, whether a youth begins a mentoring program with this protective factor in place may very well influence the outcomes he or she experiences over the course of his or her involvement in mentoring.
Symptoms of trauma exposure.
Traumatic experiences such as parental abandonment, or experiences of abuse or neglect have been identified as important potential moderators of program effectiveness.3,4,5 Traumatic experiences may be particularly important as mentoring is a relationship-based intervention, and prior negative experiences in significant relationships may influence how youth respond to a program.3
Risk factors for opioid misuse and the negative impact of others' opioid misuse.
The opioid epidemic in the United States is well-documented and involves significant numbers of youth.18 Compared to other drugs (e.g. alcohol, nicotine), less is known about the factors that influence whether or not a youth will misuse opioids or the severity of youth opioid misuse.19 Efforts are ongoing to identify factors that can reliably predict youth who are at increased risk for misusing prescription opioids with the goal of informing opioid misuse prevention and intervention efforts (see SAMSA, 2018 for a review). This section of the Toolkit includes a collection of survey items addressing a range of potential risk factors for opioid misuse among youth, including having previously been prescribed an opioid by a doctor, school grade of first opioid prescription, perceived availability of opioids, having friends who are engaged in various types of substance use, and misperceptions of risk associated with opioid use. To date, there is very little published information on the potential for mentoring relationships to reduce risk for opioid misuse among young persons. However, mentors may be well positioned to provide youth with support and guidance that serves to directly counter some sources of risk (e.g., lack of understanding of potential harmful consequences of opioid use, such as dependency or addiction). Awareness of a youth’s susceptibility to other risk factors, such as friends engaged in substance use, furthermore, may be useful as an impetus for mentors and programs to augment potentially counter-acting protective factors, such as those addressed by measures in this section of the toolkit (e.g., OST structured activity involvement).
Equally important are the ways in which youth may be susceptible to the negative effects of opioid misuse on the part of their parents or other family members as well as other adults and peers in their communities. Assessing these impacts can position mentoring programs to better support youth through enhanced staff and mentor awareness (e.g., training in trauma-informed approaches to care). This type of information also may be used to help catalyze and inform partnerships with other service providers to help address the problem of parental substance abuse and, if needed, support youth in out of home placements as a result of parental addiction.
It is also important to note that there are media reports of exposure to opioids occurring accidentally through the consumption of other drugs (e.g., methamphetamine) contaminated by or mixed with fentanyl or other opioids and this resulting in subsequent opioid misuse or addiction.20,21 There is not definitive research evidence linking accidental opioid exposure to subsequent misuse or addiction nor is there a validated measure of accidental opioid exposure. However, this topic appears to be receiving growing attention and, as knowledge and tools evolve, merits consideration by both researchers and practitioners.
i Consideration was also given to including one other risk/protective factor—impulsivity and attentiveness. However, it was ultimately decided to not include this factor in this initial version of the Toolkit for three reasons. First, self-control is already considered in the Mental/Emotional Health section of the Toolkit and should be highly correlated with impulsivity. Second, ADHD is likely to be better reported by parents (e.g., based on youth being in treatment). Finally, it can be challenging to find good public domain measures of impulsivity and attentiveness.
1. Arthur, M. W., Hawkins, J. D., Pollard, J. A., Catalano, R. F., & Baglioni, A. J. 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, 575–601. http://dx.doi.org/10.1177/019384102237850
2. Rutter, M. (1987). Psychosocial resilience and protective mechanisms. American Journal of Orthopsychiatry, 57, 316–331. http://dx.doi.org/10.1111/j.1939-0025.1987.tb03541.x
3. DuBois, D. L., Portillo, N., Rhodes, J. E., Silverthorn, N., & Valentine, J. C. (2011). How effective are mentoring programs for youth? A systematic assessment of the evidence. Psychological Science in the Public Interest, 12, 57–91. http://dx.doi.org/10.1177/1529100611414806
4. Rhodes, J. E. (2002). Stand by me: The risks and rewards of mentoring today’s youth. Cambridge, MA: Harvard University Press. 5. Rhodes, J. E. (2005). A model of youth mentoring. In D. L. DuBois & M. J. Karcher (Eds.), Handbook of youth mentoring (pp. 30–43). Thousand Oaks, CA: SAGE.
6. DuBois, D. L., Holloway, B. E., Valentine, J. C., & Cooper, H. (2002). Effectiveness of mentoring programs for youth: A meta-analytic review. American Journal of Community Psychology, 30, 157–197. http://dx.doi.org/10.1023/A:1014628810714
7. Dishion, T. J., & Medici Skaggs, N. (2000). An ecological analysis of monthly “bursts” in early adolescent substance use. Applied Developmental Science, 4, 89–97. http://dx.doi.org/10.1207/S1532480XADS0402_4
8. Elliott, D. S., Huizinga, D., & Ageton, S. (1985). Explaining delinquency and drug use. Beverly Hills, CA: SAGE.
9. Rudolph, K. D., Lansford, J. E., Agoston, A. M., Sugimura, N., Schwartz, D., Dodge, K. A., … Bates, J. E. (2014). Peer victimization and social alienation: Predicting deviant peer affiliation in middle school. Child Development, 85, 124–139. http://dx.doi.org/10.1111/cdev.12112
10. Short, J. F. Jr. (1957). Differential association and delinquency. Social Problems, 4, 233–239. http://dx.doi.org/10.2307/798775
11. Short, J. F., Jr., & Strodtbeck, F. L. (1965). Group process and gang delinquency. Chicago, IL: University of Chicago Press.
12. Van Ryzin, M. J., & Dishion, T. J. (2014). Adolescent deviant peer clustering as an amplifying mechanism underlying the progression from early substance use to late adolescent dependence. Journal of Child Psychology and Psychiatry, 55, 1153–1161. http://dx.doi.org/10.1111/jcpp.12211
13. Desjardins, T., Yeung Thompson, R. S., Sukhawathanakul, P., Leadbeater, B. J., & MacDonald, S. W. S. (2013). Factor structure of the Social Experience Questionnaire across time, sex, and grade among early elementary school children. Psychological Assessment, 25, 1058–1068. http://dx.doi.org/10.1037/a0033006
14. Downey, G., Lebolt, A., Rincón, C., & Freitas, A. L. (1998). Rejection sensitivity and children’s interpersonal difficulties. Child Development, 69, 1074–1091. http://dx.doi.org/10.2307/1132363
15. Kanchewa, S. S., Yoviene, L. A., Schwartz, S. E. O., Herrera, C., & Rhodes, J. E. (2016). Relational experiences in school-based mentoring: The mediating role of rejection sensitivity. Youth & Society. Advanced online publication. http://dx.doi.org/10.1177/0044118X16653534
16. Metzger, A., Crean, H. F., & Forbes-Jones, E. L. (2009). Patterns of organized activity participation in urban, early adolescents: Associations with academic achievement, problem behaviors, and perceived adult support. The Journal of Early Adolescence, 29, 426–442. http://dx.doi.org/10.1177/0272431608322949
17. Rose-Krasnor, L., Busseri, M. A., Willoughby, T., & Chalmers, H. (2006). Breadth and intensity of youth activity involvement as contexts for positive development. Journal of Youth and Adolescence, 35, 385–399. http://dx.doi.org/10.1007/s10964-006-9037-6
18. Center for Behavioral Health Statistics and Quality. (2017). 2016 National survey on drug use and health: Detailed tables. Substance Abuse and Mental Health Services Administration, Rockville, MD. https://www.michigan.gov/documents/mdhhs/UnderstandingWhoIsAtRisk_547024_7.pdf
19. Center for Application of Prevention Technologies (2016). Preventing prescription drug misuse: Understanding who is at risk. Substance Abuse and Mental Health Services Administration, Rockville, MD. https://www.michigan.gov/documents/mdhhs/UnderstandingWhoIsAtRisk_547024_7.pdf
20. Firth, S. (2018, July 17). Growing array of street drugs now laced with Fentanyl - Physicians, officials spotlight grim trends and possible solutions. MedPage Today. Retrieved from https://www.medpagetoday.com/primarycare/opioids/74071
21. Bebinger, M. (2019, March 21). Fentanyl-linked deaths: The U.S. opioid epidemic's third wave begins. Shots: Health News from NPR. Retrieved from https://www.npr.org/sections/health-shots/2019/03/21/704557684/fentanyl-linked-deaths-the-u-s-opioid-epidemics-third-wave-begins