Adolescent mental ill-health is a pervasive public health problem. The National Comorbidity Survey Replication–Adolescent Supplement (NCS-A) indicates that the prevalence of psychological disorders with severe impairment and/or distress is 22.2% among a nationally representative sample of U.S. adolescents age 13-18 years old (Merikangas et al., 2010). Poor mental health among adolescents is linked with impaired social interactions, violent behaviors, worse educational outcomes, substance abuse, self-harm and suicide (Brooks, Harris, Thrall, & Woods, 2002; DuRant et al., 2000; Ialongo, Werthamer, & Kellam, 1999; Paxton, Valois, Watkins, Huebner, & Drane, 2007). There is a growing awareness that pediatric psychological counseling is crucial for promoting social connections and emotional health among children, which have been identified as U.S. national priorities (Tolan & Dodge, 2005).
Despite the high prevalence of mental health problems, adolescents are reluctant to seek psychological counseling or counseling resources may not be available (Cama et al., 2017). Only 36% of U.S. youth (12-17 years old) with major depression receive treatment (Nguyen & David, 2017). Similarly, only 25% of Australian children and 20% of German adolescents meeting DSM-IV anxiety and depressive disorders criteria receive professional help (Essau, 2005; Evans & Annenberg Foundation Trust at Sunnylands., 2005).
The reasons behind the alarmingly low rates of psychological counseling use among adolescents are not fully understood. Peer social effects may play a role in adolescents’ help-seeking behavior (Gulliver, Griffiths, & Christensen, 2010; Lindsey, Joe, & Nebbitt, 2010; Moody, Feinberg, Osgood, & Gest, 2010; Pescosolido, Perry, Martin, McLeod, & Jensen, 2007). Negative perceptions of psychological counseling by peers have been implicated in low rates of psychological counseling among adolescents (Chandra & Minkovitz, 2007; Gulliver et al., 2010; Hoagwood, Burns, Kiser, Ringeisen, & Schoenwald, 2001; McKay & Bannon, 2004). Roughly 60% of adolescents with mental health needs perceive that they are treated differently or rejected by peers in their social networks (Moses, 2010). Available literature lacks a full understanding of the social peer effects experienced by adolescent users of psychological counseling.
Previous studies that link users of adolescent psychological counseling with peer rejection suffer from two important methodological concerns. First, prior studies do not control for alternative explanatory mechanisms by which adolescents choose or reject their friends (e.g., selection and influence). Adolescent friendships may be shaped by social selection, or homophily, the tendency to select friends with similar characteristics (de la Haye, Robins, Mohr, & Wilson, 2011; Essau, 2005; Kiuru, Burk, Laursen, Nurmi, & Salmela-Aro, 2012; Kiuru, Burk, Laursen, Salmela-Aro, & Nurmi, 2010; Mercken, Steglich, Knibbe, & De Vries, 2012; Mercken, Steglich, Sinclair, Holliday, & Moore, 2012; Mercken, Sleddens, de Vries, & Steglich, 2013; Shoham et al., 2012; Van Zalk, Kerr, Branje, Stattin, & Meeus, 2010). As such, an adolescent might base friendship selection or rejection not only on help-seeking behavior, but also on similarities in personal attributes (e.g. cognitive ability, alcohol use, or sports).
Second, prior studies rely on traditional statistical methods (e.g., regression, structural equation models) that do not control for the interdependent nature between adolescent help-seeking behavior and friendship formation. Socialization (or influence), the tendency to adopt behaviors, attitudes, or norms of their friends, may contribute to adolescent help-seeking behavior (Prinstein, 2007). Adolescent recipients of psychological counseling may impact others’ help-seeking behavior. Influence effects in peer groups create dependency pathways between friendship formation and the use of psychological services, and thus, violate the independence assumptions of commonly used statistical methods (Shalizi & Thomas, 2011).
A recent analytical approach to the analysis of friendships and psychological counseling use is stochastic actor-based modeling (Snijders, van de Bunt, & Steglich, 2010), which allows for selection and influence effects in the same model. Stochastic actor-based modeling simulates the co-dependence of friendship formation and help-seeking behavior from an initial data state to a final data state while simultaneously accounting for social selection and influence effects. Stochastic actor-based model studies have been successfully applied to the study of friendship formation and adolescent alcohol use, tobacco use, exercise, and depression in peer social networks (de la Haye et al., 2011; Kiuru et al., 2012; Kiuru et al., 2010; Mercken et al., 2012; Mercken et al., 2012; Mercken et al., 2013; Shoham et al., 2012; Van Zalk et al., 2010). To the best of our knowledge, no prior research has specifically focused on peer effects and help-seeking behavior while controlling for selection and influence effects in a single model. Without clear guidance on the causal pathways between adolescent friendship ties and adolescent help-seeking behavior, interventions for improving adolescent mental health may be ill-informed.
The present study investigates how adolescent peer friendship formation relates to help-seeking behavior among U.S. adolescents while simultaneously accounting for selection and influence effects. We use stochastic actor-based modeling to test the following research questions:
Research Question #1: How does adolescent psychological counseling use relate to peer friendship formation and maintenance?
Research Question #2: How does friendship network structure influence the uptake of help-seeking behaviors?
Low friendship network stability between study waves raises doubts about the reliability of the friendship data reported and may heighten the possibility of convergence failure in the iterative estimation process. The Jaccard index (Real & Vargas, 1996) measures network stability as the proportion of friendship ties within each school that remain unchanged from Wave I to Wave II out of the total number of ties reported at either wave. A Jaccard index of 0.20 or higher is generally required for inclusion in a stochastic actor-based model analysis (Ripley et al., 2011). A total of 7 schools did not meet the Jaccard index criteria and were excluded from the analysis. One possible explanation for low network stability is that friendships modulate when students transition from middle school to high school. Schools that were excluded from the analysis were smaller schools (mean sample size of 71 students), and a majority were middle schools (5 out of 7).
Variables were chosen
where β is the parameter set,
In psychological counseling change estimation, the student
where β is the parameter set for psychological counseling use,
The analysis uses the statistical program RSIENA (Ripley et al., 2011), originally designed by Snijders and van Duijn (Snijders & Van Duijin, 1997) and programmed by Ruth Ripley and Krists Boitmanis.
Furthermore, the friendship change part of the model includes structural network effects on friendships: (1) reciprocal friendship nominations (
The psychological counseling change part of the model (i.e., influence effects) evaluates how the number of friendship nominations that an adolescent receives impacts adolescent psychological counseling use. The psychological counseling change model controls for linear trends in psychological counseling use over time as well as age, gender, race/ethnicity, alcohol use, depression score, self-rated physical health, sensation seeking, 3-step reach and local density. Social networks tend to display “three degrees of influence,” such that behaviors within them spread beyond the closest social ties to one- or two-step removed social connections (Christakis & Fowler, 2013). An adolescent’s 3-step reach is a count of the number of school classmates who can be reached by three or fewer steps via friendship ties (Valente, 1995). Local density is calculated as the density of connections within the 3-step reach network.
Finally, we employ the Snijders-Baerveldt meta-analysis test (Snijders & Baerveldt, 2003) to test overall significance of the primary and control variables aggregated across schools. The Snijders-Baerveldt test considers the studied schools to be a sample of a population of schools and makes inference about parameters across all schools, taking into account the variability associated with sampling of schools, which adds to the width of the confidence intervals.
The sample consisted of 2,264 adolescents in grades 7 through 11 at Wave I of the Add Health survey (Table 1). Half (49%) of the respondents were minorities, with 18% Hispanic and 16% African American. Respondents to Wave II included 2,044 adolescents, 90.3 percent of the Wave I sample. There were no significant differences between respondents and non-respondents in age, grade, gender, race/ethnicity, scholastic aptitude, or use of psychological counseling.
Descriptive Statistics of Add Health Sample, Wave I, 1995 (N=2,264)
Male (%) | 51.2 |
Age, mean (sd) (years) | 15.8 (1.3) |
Age, range (years) | 12-18 |
Grade Level (%) | |
7th grade | 6.7 |
8th grade | 6.8 |
9th grade | 16.2 |
10th grade | 35.8 |
11th grade | 34.5 |
Race (%) | |
Non-Hispanic white | 50.5 |
Black | 16.3 |
White Hispanic | 18.5 |
Other | 14.8 |
Depression (modified CES-D score), mean (sd) | 11.9 (7.5) |
Self-rated physical health (%) | |
Excellent | 25.7 |
Very good | 39.6 |
Good | 26.7 |
Fair | 7.7 |
Poor | 0.5 |
Frequency of alcohol consumption, past 12 months (%) | |
None | 49.6 |
1-2 times | 18.3 |
3-12 times | 14.2 |
More than monthly, less than weekly | 8.3 |
Weekly or more often | 9.6 |
Add Health Picture Vocabulary Test, mean (sd) | 99.1 (13.2) |
Bike/skate/car/boat racing frequency (%) | |
Never | 45.4 |
Once or twice | 23.7 |
Once a month or less | 7 |
Two or three times a month | 6.5 |
Weekly or more often | 17.4 |
Mother’s education (%) | |
Less than high school | 20.5 |
High school graduate | 45 |
Some college | 12.6 |
Graduated college | 21.8 |
Table 2 provides unadjusted psychological counseling use at Wave I by individual characteristics. Overall, 8.7% of study subjects had received psychological counseling in the past year. In unadjusted univariate analyses, psychological counseling was more common among females (11% vs. 7%), non-Hispanic whites (11% vs. 6 %) and among students who had consumed alcohol in the past year (12% vs. 5% for abstainers). 21% of the students with modified CES-D scores of 24 or higher had received psychological counseling in the past year. The mean CES-D score for the study sample did not change significantly from Wave I to Wave II (11.9 vs 11.8, respectively).
Wave I Psychological Counseling Use by Individual Characteristics (N=2,264)
Individual Characteristic at Wave I | n per category | Psychological counseling use within category in past year at Wave I (% of n) | ||
---|---|---|---|---|
All Subjects | 2,264 | 8.7 | ||
Gender | Male | 1,159 | 6.9 | |
Female | 1,105 | 10.6 | ||
Age | 12-13 years | 153 | 5.9 | |
14-15 years | 647 | 10.2 | ||
16-17 years | 1,339 | 8.4 | ||
18-19 years | 125 | 7.3 | ||
Race | Non-Hispanic white | 1,144 | 11.3 | |
Black | 368 | 7.4 | ||
White Hispanic | 418 | 5.7 | ||
Other | 334 | 5.4 | ||
Modified CES-D depression score | 0-23 | 2,096 | 7.7 | |
24 or more | 168 | 21 | ||
Self-rated physical health | Excellent | 580 | 7.8 | |
Very good | 897 | 7.4 | ||
Good | 604 | 10.8 | ||
Fair/Poor | 183 | 11.5 | ||
Frequency of alcohol consumption, past 12 months | None | 1,138 | 5.3 | |
1-2 times | 410 | 11.2 | ||
3-12 times | 316 | 11.1 | ||
More than monthly, less than weekly | 185 | 14.1 | ||
Weekly or more often | 215 | 14 | ||
Bike/skate/car/boat racing frequency | Never | 1,028 | 8.4 | |
Once or twice | 536 | 8.3 | ||
Once a month or less | 158 | 9.1 | ||
Two or three times a month | 148 | 13.3 | ||
Weekly or more often | 394 | 8.4 | ||
Mother’s education | Less than high school | 464 | 8.8 | |
High school graduate | 1,019 | 8.4 | ||
Some college | 287 | 9.8 | ||
Graduated college | 494 | 8.5 | ||
Friendship nominations received | 0-1 | 491 | 8.3 | |
2-3 | 668 | 9 | ||
5-6 | 601 | 8.5 | ||
7+ | 504 | 8.4 | ||
Friendship nominations sent | 0-1 | 496 | 7.4 | |
2-4 | 669 | 8 | ||
5-6 | 482 | 12.1 | ||
7+ | 617 | 7.4 | ||
3-step friendship reach | 0-14 | 583 | 7.4 | |
15-42 | 559 | 6.9 | ||
43-76 | 559 | 9.1 | ||
77+ | 563 | 11 | ||
Local friendship density | 0-0.20 | 563 | 10.1 | |
0.21-0.29 | 546 | 9.9 | ||
0.30-0.39 | 590 | 7.3 | ||
0.40-1.00 | 565 | 7.3 |
p<0.05
p<0.01
p<0.001
Figure 1 displays the range, 25th percentile, and 75th percentile for β parameter estimates of friendship and psychological counseling use change aggregated across the nine schools during a one-year follow-up. Statistically significant dynamics for friendship selection included psychological counseling users sending more friendship nominations. Friendship nominations were associated with similarities in age, gender, race, scholastic aptitude, and alcohol use. Individuals meeting the criteria for depression sent fewer nominations and received fewer nominations. Statistically significant factors for psychological counseling use (i.e., influence) were female gender, depression, and alcohol use.
The top portion of Table 3 presents the stochastic actor-based model results for
Parameter Estimates for Actor-Based Model of Friendship Network and Psychological Counselinga
Psychological counseling effect on nominations sent | 0.50** | 0.14 | 3.57 |
Psychological counseling effect on nominations received | −0.08 | 0.13 | −0.62 |
Psychological counseling similarity | 0.32 | 0.15 | 2.13 |
Outdegree | −3.14*** | 0.34 | −9.24 |
Reciprocity | 2.44*** | 0.2 | 12.19 |
Transitive triplets | 0.84*** | 0.09 | 9.33 |
3-cycles | −0.46* | 0.15 | −3.07 |
Age similarity | 1.19* | 0.42 | 2.83 |
Same gender | 0.34*** | 0.04 | 8.52 |
Same race/ethnicity | 0.39** | 0.13 | 2.98 |
AHPVT similarity | 0.65*** | 0.1 | 6.46 |
Alcohol use similarity | 0.66*** | 0.08 | 8.24 |
Depression effect on nominations sent | −0.21* | 0.09 | −2.31 |
Depression effect on nominations received | −0.08* | 0.04 | −2.21 |
Depression similarity | 0.14 | 0.13 | 1.07 |
Tendency (linear) | −3.35*** | 0.42 | −7.96 |
Friendship nominations received | 0.13 | 0.07 | 1.83 |
Friendship nominations sent | 0.03 | 0.12 | 0.21 |
Psychological counseling use by friends | 0.02 | 0.1 | 0.18 |
Local density | −0.11 | 0.36 | −0.31 |
3-step reach | 0 | 0.08 | −0.04 |
Age | −0.1 | 0.19 | −0.53 |
Gender (male) | −0.88* | 0.24 | −3.61 |
Race (white) | 0.03 | 0.07 | 0.41 |
Depression | 1.34* | 0.45 | 2.94 |
Alcohol use | 0.37* | 0.13 | 2.85 |
Sensation seeking | 0.18 | 0.09 | 2.02 |
Self-rated physical health | 0.02 | 0.12 | 0.15 |
Mother’s education | 0.05 | 0.18 | 0.29 |
Est.
p<0.05
p<0.01
p<0.001
Adolescents in more interconnected social networks generated more friendship connections. Social network structural characteristics of
Similar to previous research (Brendgen, Vitaro, Turgeon, & Poulin, 2002), adolescents with higher depression scores nominated fewer classmates as friends (
In addition, as reported in other studies (de la Haye et al., 2011; Kiuru et al., 2010; Mercken et al., 2012), students were more likely to choose friends of similar age (
The second part of Table 3 provides factors which affected use of psychological counseling. The number of friendship nominations an adolescent received or sent was not associated with increased psychological counseling use (
Figure 2 provides a visual representation of the help-seeking behavior at Wave I of one of the schools in the study. In this school, boys are represented as squares and girls as circles. Students who had received psychological counseling are colored red. Students who did not receive counseling are colored blue. The nodes are sized according to the number of incoming friendship nominations. As seen in the figure, girls were more likely to have received counseling than boys. There was no significant difference in friendship nominations based on receiving counseling. In addition, the students who received counseling were not significantly clustered together or isolated from the other students.
The main goal of this investigation is to investigate the co-dependence between adolescent friendships and help-seeking behavior while accounting for social selection and influence effects in the same model. Specifically, the study evaluates: (1) how adolescent use of psychological services relates to friendship formation and maintenance in peer social networks; and (2) how friends and friendship network structure influence the uptake of help-seeking behaviors. Our results suggest that selection effects may be more prominent than influence effects in the relationship between the use of psychological counseling and adolescent friendship formation. Adolescents increase their number of friendship nominations sent and remain steady in the number of nominations received following psychological counseling. On the other hand, the study findings offer little support to the theory that friends or friendship group structures influence the receipt of psychological counseling. Our study is the first to employ stochastic actor-based modeling of friendship formation and help-seeking behavior in a sample of U.S. middle and high school students.
Our findings show that receiving adolescent psychological counseling in the past year resulted in a 65% increase in an adolescent’s naming classmates as friends compared to otherwise similar adolescents who did not seek psychological help. It is encouraging that adolescent users of psychological counseling are actively engaging with their peers and are seeking out social support to lessen the burden of psychological distress. Our data points to the benefits associated with the use of adolescent psychological services, and advocates for availability of wide-spread screening and early detection and treatment of mental ill-health among U.S. adolescents.
In addition, our results show that after adjusting for covariates and selection and influence effects, help-seeking behavior did not contribute to a loss of friends (i.e., no difference in being named by classmates as a friend) at rates higher than exhibited by adolescents with no psychological services use. To reconcile our findings with studies on stigma reported by users of psychological counseling (Chandra & Minkovitz, 2007; Moses, 2010), it is possible that the perception of peer rejection associated with psychological counseling is associated with adolescents’ individual characteristics which inhibit friendship formation (e.g. poor social skills), and not with negative peer reactions to psychological counseling services per se. Future research may wish to explore this issue further.
Furthermore, adolescents who resided in more interconnected peer social networks (i.e., networks with a greater tendency for friends of friends to be named friends) made more friends. An adolescent in need of psychological counseling would be twice as likely to be named as a friend in a more interconnected peer social network than an otherwise identical adolescent in a less closely knit peer group. In more interconnected networks, adolescents with mental health needs may come into more social contact with peers, which may help “break down barriers” and promote better mental health (Brendgen, Vitaro, Doyle, Markiewicz, & Bukowski, 2002; Estroff, Penn, & Toporek, 2004; Martin, Pescosolido, Olafsdottir, & McLeod, 2007). Our results argue for group-wide structural social peer effects that may mitigate the affliction of adolescent mental health illness.
The presence of social network structural effects on friendship formation lends support to interventions targeting greater interconnectedness of adolescent peer groups as a way to attenuate the experience of adolescent mental illness by harnessing peer social support. Educational interventions may focus on developing better social skills among all adolescents as a way to promote peer group interconnectedness, and, thus, improving the social functioning of adolescents with psychological counseling needs. Our results lend credence to recent efforts to teach social skills in primary and secondary education (Durlak, Weissberg, Dymnicki, Taylor, & Schellinger, 2011), which may enhance mental health and social well-being for vulnerable adolescents in need of psychological counseling.
The study has a number of strengths. Our investigation is the first to employ stochastic actor-based modeling for disentangling peer selection and influence effects on psychological counseling in a sample of U.S. middle and high school students. The analysis examined a large sample of U.S. adolescents across a variety of school sizes and settings. It included a great variety of control variables for individual characteristics and controls for selection and influence effects in the same model.
The study results should be viewed in light of its potential limitations. First, adolescents’ friendship nominations were limited to 10 friends, which may bias the friendship formation parameters in the model. However, studies show that students report having an average of four friends (Burk, Steglich, & Snijders, 2007). Second, the analysis focused on the social connections within a school and may not have captured all peers in the adolescent social network. Students who had friends outside of school may have been misrepresented in the analysis and the inclusion of those alters could possibly have shaped the findings. It is possible that non-school based friendships may be an important link to adolescents’ seeking help for depressive symptoms. However, school-based networks may be most pertinent for intervention efforts. Third, schools with low network stability were excluded from the analysis. The study results may only apply to school settings where friendships are durable. Fourth, psychological counseling was self-reported with a single question. However, a similar outcome measure of psychological counseling has been commonly employed in other studies of mental health utilization (Pirkis et al., 2003). Finally, the analysis uses the Add Health data set which was initiated in 1995. There may be substantive differences in how participation in mental health services is viewed now as compared to how it may have been viewed 20 years ago. However, the disparity between the need for mental health services in adolescence and the receipt of counseling and other mental health treatments among adolescents appears to be consistent since the initiation of the Add Health study (Kataoka et al., 2002; Cama et al., 2017).
Our findings argue for improved social functioning of adolescent psychological counseling users. The results point to the necessity of wide-spread screening, early detection, and treatment of mental ill-health among U.S. adolescents. Effective policy can support evidence-based interventions, modern training programs, and tangible strategies for improving both social skills and adolescent mental health. Group interventions targeting building social skills to enhance peer group social network interconnectivity may promote better social functioning for adolescents in need of psychological counseling. Our results may be of interest to policymakers, public health professionals, educators, parents, and community leaders who focus on improving adolescent mental health.