Crossing the Age Divide: Cross-Age
Collaboration Between Programs Serving
Transition-Age Youth
Maryann Davis, PhD
Nancy Koroloff, PhD
Kathryn Sabella, MA
Marianne Sarkis, PhD
Abstract
Programs that serve transition-age youth with serious mental health conditions typically
reside in either the child or the adult system. Good service provision calls for interactions
among these programs. The objective of this research was to discover programmatic
characteristics that facilitate or impede collaboration with programs serving dissimilar age
groups, among programs that serve transition-age youth. To examine this Bcross-age
collaboration,^ this research used social network analysis methods to generate homophily
and heterophily scores in three communities that had received federal grants to improve
services for this population. Heterophily scores (i.e., a measure of cross-age collaboration) in
programs serving only transition-age youth were significantly higher than the heterophily
scores of programs that served only adults or only children. Few other program markers or
malleable program factors predicted heterophily. Programs that specialize in serving
transition-age youth are a good resource for gaining knowledge of how to bridge adult and
child programs.
Address correspondence to Maryann Davis, PhD, Transitions to Adulthood Center for Research, Systems and
Psychosocial Advances Research Center, Department of Psychiatry, University of Massachusetts Medical School, 222
Maple Ave., Shrewsbury, MA 01545, USA. .
Kathryn Sabella, MA, Transitions to Adulthood Center for Research, Systems and Psychosocial Advances Research
Center, Department of Psychiatry, University of Massachusetts Medical School, Shrewsbury, MA, USA.
Nancy Koroloff, PhD, Regional Research Institute, School of Social Work, Portland State University, PO Box 751,
Portland, OR, USA.
Marianne Sarkis, PhD, International Development and Social Change, Global and Community Health Program, Clark
University, Worcester, MA, USA.
)
Journal of Behavioral Health Services & Research, 2018. 356–369. c 2018 National Council for Behavioral Health. DOI
10.1007/s11414-018-9588-9
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Introduction
An estimated 3.8 million 15–25-year-olds (aka transition-age youth) in the USA are living with
serious mental health conditions.*1–3 Transition-age youth with serious mental health conditions often
struggle to complete their high school education,4–6 enroll and complete college,7–9 and establish adult
work lives.10 They are also at high risk for homelessness,11, 12 justice system involvement,13, and cooccurring substance use disorders14 and commonly receive services in multiple public child and adult
systems such as mental health, justice systems, foster care, or vocational rehabilitation. These service
systems are often organized by the age group of the target population they serve. For example, child
welfare services are typically provided up to age 18 or 21 years15 while adult mental health services are
offered to those 18 and older.16 Therefore, services that transition-age youth encounter are found in both
child and adult systems, and for 18–21 year olds, often in both simultaneously. In addition to adult and
child systems serving different target populations with different eligibility criteria,16 variable funding
streams, accountability structures, different service Bcultures,^ and different staff training backgrounds17 produce further system fragmentation. As a result, strong collaboration between these
different services is needed to most effectively serve transition-age youth, to coordinate services when
both systems are involved, to facilitate transition from child to adult services, to reduce gaps and
redundancies in services, and to prevent service dropout. Unfortunately, the coordination among and
transition between child and adult systems is not easy.18
More broadly, services can improve and benefit when child and adult systems work together, such as
exchanging expertise that one system has and the other needs. For example, family involvement during
young adulthood can be critically important to young adult success yet it is quite complicated. Child
mental health providers have expertise in engaging or partnering with families that could help inform
adult programs on how to maximize age-appropriate family support for young adults.19, 20 Conversely,
adult mental health services have expertise supporting individuals’ vocational goals,21, 22 which could
help inform child systems working with older adolescents who want to work. For these and additional
reasons, it is important to support and foster more cross-age collaboration, i.e., collaboration between
two programs that serve different age groups that could include transition-age youth.
The Substance Abuse and Mental Health Services Administration (SAMHSA) acknowledges the
need to promote collaboration across child and adult services to improve the system that supports
transition-age youth with serious mental health conditions (e.g., SAMHSA RFA No. SM-1401723). Based on this need, SAMHSA has initiated three separate grant programs that encourage
grantees to promote collaboration across child and adult services: the Partnership for Youth
Transitions (2002–2006), the Emerging Adult Initiative (2009–2014), and the Now is the Time
Healthy Transitions program (2014–2019). The evaluation of the Partnership for Youth Transitions
initiative documented success in the development of program models; findings suggested that interorganizational collaboration, especially among providers of adult and child mental health services,
was potentially key to positive outcomes.24 Yet, little is known about the types of programs that
collaborate across this particular age divide and the malleable program factors (i.e., those that can
be changed) associated with greater cross-age collaboration.
Knowledge from collaboration in other sectors can inform our understanding of what types of
programs may collaborate well across this particular age divide and the potential malleable
program factors associated with greater cross-age collaboration. One factor identified as
contributing to health/human services collaboration is program specialization. Generalist and
specialist programs can serve a similar population but generalists, unlike specialists, offer
This figure is calculated from the 12% prevalence rate of serious emotional disturbance for
children and adolescents1 applied to population of 15–17-year-olds and 6.5% prevalence rate of
serious mental illness2 applied to the population of 18–25-year-olds from in the 2016 U.S. Census
population estimate.3
*
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DAVIS ET AL.
357
comprehensive services to the same population in-house minimizing their need to refer clients to
outside sources. Generalist organizations tend to be bigger, have more resources, and serve more
clients than specialist organizations. They tend to be more central to the network of organizations
serving a population by serving a brokerage or linking role with other resources in the network.25
On the other hand, organizations may be more specialized by virtue of the population they serve,
by their capacity, or the focus of their treatment (e.g., small residential treatment program for 16–
21-year-olds). Specialized organizations tend to be more limited in resources and will refer clients
to bigger and more generalized organizations as a way to best benefit their clients. Thus, specialist
programs may collaborate more because other organizations have resources their organization or
clients need. Moreover, limitation in any single aspect of a program’s scope (e.g., funding level,
number of clients, and array of services) is one of the main predictors of the types of linkages that
organizations maintain with other organizations.25, 26
Other researchers have found that collaboration occurs when it is needed to meet client
needs26, 27 and that reciprocal connections are often based on exchanges or competition. For
example, connections may be forged in order to obtain funding or specific services that are
needed to provide improved services or to fulfill a contractual obligation. Perceptions that
collaboration is expected can also increase collaborative behavior.28 Thus, a program’s
perception that clients will benefit from cross-age collaboration or that a funder requires
cross-age collaboration should result in enhanced cross-age collaboration. Contractual or
funding obligations that also include requirements for collaboration may increase program
collaboration efforts. Other research in a variety of organization types (e.g., education,
manufacturing, human service29–32) has described other mechanisms that foster collaboration
and system change that might be present in programs that collaborate more. These include
programs or groups having overlapping responsibilities, reward/accountability based on
collective performance,29–33 mechanisms that make it easy to understand what each other is
doing, and clear procedures that foster collaboration.29 Having shared perceptions and clear
communication from leadership are also key to system change.34–36
While the general literature about collaboration is well established, little is known about
programs that collaborate across age divides or across sectors, particularly when serving the needs
of transition-age youth with serious mental health conditions. Understanding which types of
programs collaborate more could help system reformers identify potential partners. Understanding
malleable program factors (i.e., those that can be changed) associated with greater cross-age
collaboration could also provide guidance for incentivizing or targeting system reform policies.
The present study utilized social network analysis to examine program markers and malleable
factors within programs that serve transition-age youth with serious mental health conditions as
correlates of cross-age collaboration. Social network analysis has been used extensively in
organizational studies as a way to understand collaboration and competition in coalition-building,
alliance formation, and referral networks.27, 37–39 It describes which organizations are in a network
or system, each organization’s characteristics, and the strength and direction of each organization’s
relationship to other network organizations.37, 40 Social network analysis data collection
methodology was established for mental health organizational systems by Morrissey and
colleagues.37, 41 Details of applying this methodology to systems serving transition-age youth
were published previously.18 Specifically, this study was designed to address three hypotheses
about the association between cross-age collaboration rates and program markers and malleable
characteristics that reflect previous findings on collaboration:
1. Programs’ demographic characteristics (markers) will be associated with different cross-age
collaboration rates. Specifically, the following types of programs will have higher cross-age
collaboration rates: programs serving only transition-age youth vs. programs serving only
children or only adults; larger vs. smaller programs; younger vs. older programs; and more
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vs. less specialized programs. The association of type of service provided (e.g., mental health
vs. substance abuse) with cross-age collaboration was also explored.
2. Programs’ staff members’ perceptions regarding cross-age collaboration (malleable factors)
will be associated with cross-age collaboration rates. Specifically, staff perceptions that crossage collaboration is achievable and that system level factors support or encourage cross-age
collaboration (e.g., there are reward or accountability systems that encourage cross-age
collaboration) will be associated with higher levels of cross-age collaboration.
3. Programs with higher levels of general within- or across-program mechanisms of
collaboration will have higher rates of cross-age collaboration.
Methods
Recruitment
In social network analysis methods, respondents are recruited because they have been identified
as belonging to the network of interest.42 Two SAMHSA-funded Emerging Adult Initiative
grantees43 and one Partnership for Youth Transition24 grantee served as research sites. One site was
a medium-sized city of 600,000, and two were suburban metropolitan areas (populations of
345,000 and 240,000), each in a different state. Each site provided one knowledgeable community
informant who was deeply familiar with services for transition-age youth in their community to
identify programs in the network. To make the networks comparable in the three sites, criteria for
inclusion and exclusion of programs were developed and applied across all sites. Priority was given
to programs that directly provided mental health and related services to individuals with serious
mental health conditions between the ages of 15 and 25 in the geographic area targeted in the grant
activities. Programs could serve all or part of this age range. Because of the focus on cross-age
collaboration, programs that served all ages were excluded. Knowledgeable community informants
were provided a list of program types often found in child or adult service delivery networks for
transition-age youth with serious mental health conditions (e.g., mental health, substance abuse,
vocational and educational supports, independent living, or housing supports). Community
informants were then asked to generate a list of the specific programs in the geographic area
targeted by their SAMHSA grant that could serve individuals between ages 15 and 25 with serious
mental health conditions.
Named programs were sent an invitation letter and fact sheet via email and postal mail
describing the study and asking them to designate a key informant from their program to
participate in the study. The key informant for a program was described as an individual who
occupied a boundary-spanning role that would make him/her knowledgeable about the practices
within his/her program and the working relationships of his/her program with other community
programs.40 Key informants were then invited to complete a one-time web survey and phone
interview that assessed information about the program.
A total of 87 programs were identified across the three grant locations. In grant location A,
26 eligible programs were identified and 20 key informants were successfully recruited to
complete the phone interview and web survey (3 were non- responders after repeated
attempts, 2 declined participation, and 1 program was de-funded during the recruitment
period). In grant location B, 28 eligible programs were identified and 21 key informants were
successfully recruited (2 were non-responders after repeated attempts, 3 declined participation, 1 required a lengthy IRB application that was not feasible, and 1 program was defunded during the recruitment period). In grant location C, 33 eligible programs were
identified and 28 key informants were successfully recruited (3 were non-responders after
repeated attempts, 2 declined participation). The overall response rate was 80%. The 20%
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DAVIS ET AL.
359
missing response rate had a small to negligible effect on examining the overall network, as
many of the missing agencies were peripheral to the network.44–46
Instruments and Measures
The structured telephone interview consisted of three sections. Sections 1 and 2 were
comprised of standard social network questions for mental health systems.37 To examine
Hypothesis 1, Section 1 asked eight multiple choice questions about the program’s demographic
information, the services it provided, and individuals served. Questions were added to standard
social network analysis program characteristics questions that asked about the age groups served
by the program (e.g., BPlease estimate the age composition of your program’s clients based on
the unduplicated count above^). Section 2 was used to measure the main outcome variable of
collaboration and included standard social network analysis system questions37, 41 which query
about the interaction of the program with each program in the network in meeting for client
planning purposes, meeting to discuss issues of mutual interest, sending referrals, receiving
referrals, and sharing resources. Respondents answered these questions using a five-point Likerttype scale (ranging from 1, Not at All, to 5, Very Often). Section 3 asked respondents to endorse
each specific service their program provided and then to endorse the age groups it was provided
to (paralleling the age groups in the age question in Part I).18 The age group that each program
served was identified from the questions in Part I and Part III regarding ages served. Based on
these questions, programs were categorized into one of three groups of ages served: child only
(under age 18 or 21), transition-age youth only (within the range of ages 15–25), or adult only
(ages 18 and older). Analyses were limited to programs that clearly served one of these
populations; seven programs in the study were identified as serving all ages and were removed
from further analyses.
The web survey included two measures (available from the authors). To examine Hypothesis 2,
the first web survey measure was a constructed questionnaire about perceptions regarding
collaboration challenges and leadership perceptions or attitudes about collaboration (specific items
are listed in Table 3). Individual items were rated on a Likert scale of 1–6 (1 = Strongly Agree, 6 =
Strongly Disagree) and the answers were collapsed to represent agreement (scores of 1–3) and
disagreement (scores of 4–6).
To examine Hypothesis 3, the second web survey measure was the Mechanisms of
Collaboration Questionnaire (MoCQ), a constructed questionnaire assessing program practices
that represent mechanisms for collaboration. These were asked about within-program practices
(e.g., BJobs in my program have overlapping responsibilities^) and across-program practices
(e.g., BWe share responsibility with other programs for the well-being of clients that we share^).
Responses to both were rated on a Likert scale of 1–6 with lower scores indicating better
collaboration. The MoCQ yields two scale scores: Collaboration Mechanisms within Programs
(intra-program) has nine items and Collaboration Mechanisms across Programs (inter-program)
has ten items.
Analytic Approach
While traditional social network analysis characterizes the whole network and its subgroups, the
current research explores dyadic relationships (i.e., relationships between any two programs) across
the networks in each of the three research sites. Specifically, this research explores cross-age dyadic
relationships in which a program connects with another program that serves a different age (i.e.,
cross-age collaboration) across five activities of collaboration: sending referrals, receiving referrals,
sharing resources, meeting for client planning purposes, and meeting for shared interests. In
addition to establishing the extent to which cross-age collaboration exists, this study also explores
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program characteristics and malleable factors that are correlated with cross-age collaboration.
Because of this focus, the dependent variable is based on two constructs: the age category of the
program (i.e., what ages the program serves) and the degree to which the program collaborates with
programs that serve other ages.
For social network analysis, each program’s relationship with each other program in their
network was dichotomized into unconnected or connected for each of the five questions. Key
informant responses of Don’t Know, Not at All, and Rarely were categorized as unconnected
between programs while responses of Occasionally, Fairly Often, and Very Often were
categorized as connected. Consistent with typical social network methodologies, responses to
each question were included in a square matrix with binary data that indicated the presence or
absence of a relationship (connected or unconnected) between any two agencies. A matrix was
created for each question resulting in five matrices. Using UCINET 6.0,47 the five matrices were
summed into one aggregate valued matrix indicating the strength, or weight, of the relationship.
Homophily,48 a common measure in social network analysis, refers to the similarities in
attributes or dimensions that exist among members of a network. In social network analysis, the
homophily ratio is calculated by counting the number of a program’s connections with sameattribute programs (i.e., ages served) and dividing by the total number of the program’s
connections. Thus, heterophily, the inverse of homophily, is the extent to which programs
connected with programs serving a different age group. Percent heterophily is calculated by
number of ties between an agency and another in a different attribute category divided by the
agency’s total number of ties:
H% ¼
E
T
where H% = percent heterophily, E = external connections (or cross-age attribute connections), T =
total number of connections.
For example, in the above formula, the number of connections a program that only serves
children has with programs that serve transition-age youth only or adults only is represented by E.
The total number of programs with which they have a connection is represented by T.
For this analysis, the homophily algorithm in UCINET 6.0 for Windows47 was used to analyze
the similarities of all outgoing relationships (i.e., relationship that a program reported they had with
another program and not the reverse) for each program in the network regarding the ages served
attribute. The inverse of that value (i.e., outgoing heterophily) was used to measure the level of
cross-age (i.e.,, different ages served attribute) outgoing collaboration. In this sense, the heterophily
measure could be used to identify cross-age collaborations, the extent to which organizations
collaborate with agencies that serve different age groups from their own.25 A program’s heterophily
score can range from 0 to 1 and is interpreted in this study as the percent of all of the outgoing
connections in a network that are with cross-age programs. For example, a heterophily score of 0.6
indicates that 60% of all connections that program has to other programs are with programs that
serve a different age group than it does.
Analysis of variance was used to examine predictor variable group differences in heterophily
scores (Hypotheses 1 and 2), and Pearson r was used to examine the relationship between
continuous predictor variables and heterophily scores (Hypothesis 3).
All three grant locations had programs in each of the three ages served categories. However,
since programs serving transition-age youth only are a relatively new phenomenon18, they may not
exist in most communities. In order to understand cross-age collaboration in more traditional child
only and adult only programs, analyses were conducted first with all programs and then by
excluding the transition-age youth only programs. This analytical process allowed us to examine
factors that were relevant to child only and adult only programs without the effect of transition-age
youth only programs.
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361
Results
Program Descriptions
Table 1 presents frequencies for the characteristics of the 69 programs. Comparable proportions
were child only and adult only, with a smaller proportion being transition-age youth only. As can
be seen from Table 1, there was a wide dispersion of program sizes (i.e.,, number of clients served
per year) and a wide variety of services were offered. Across all programs in all three locations, the
mean heterophily score was the middle of possible scores (Mean = 0.49 ± 0.27 SD). The three
communities were located in three different states, so while there was a weak effect of grant
location on heterophily scores (p G .05), post hoc analyses revealed no significant differences
between any two pairs of grant locations on variables of relevance to this study. For this reason,
data were combined across all three locations for all remaining analyses.
Programs’ Demographic Characteristics Will Be Associated with Different Cross-Age Collaboration Rates
Heterophily scores were markedly different depending on the ages a program served. Transitionage youth only programs had significantly higher heterophily scores (X = 0.69 ± 0.18 SD) than
either child only (X = 0.44 ± 0.21 SD) or adult only programs (X = 0.42 ± 0.32 SD; F (2, 66) =
6.22, p G .005). These heterophily scores demonstrate the high proportion (69%) of connections
that transition-age youth only programs have with programs serving other age groups, whereas
fewer than half of the programs that child only and adult only programs connected with served
other age groups. When transition-age youth only programs were removed from the analysis, mean
heterophily scores dropped (X = 0.28 ± 0.24 SD) and heterophily scores were not significantly
different between child only (X = 0.26 ± 0.20 SD) and adult only programs (X = 0.31 ± 0.29 SD;
t(df = 49) = 0.65, p 9 .10). Thus, something in the nature of programs serving only transition-age
youth contributes to high levels of collaboration with programs serving other age groups.
Table 1
Participating program characteristics
Variable
Ages served (N = 69)
Child only (G 18)
Transition-age only
(primarily 16–25)
Adult only (primarily 18 and older)
Years providing services (N = 65)
0 to 10 years
11 to 19 years
Over 20 years
Number of clients served in
FY 2012 (N = 64)
Less than 50 clients
51 to 199 clients
200 to 1000 clients
Over 1000 clients
362
n (%)
Variable
n (% Yes)
28 (41%)
15 (22%)
Services provided
Mental health service
Housing service
34 (50%)
22 (32%)
26 (38%)
28 (42%)
21 (31%)
19 (27%)
11
22
18
13
Independent living service
Vocational service
Substance abuse services
Education services
Recreation services
Delinquency rehabilitation
services
20 (29%)
18 (27%)
18 (27%)
15 (22%)
12 (18%)
9 (13%)
(18%)
(33%)
(29%)
(20%)
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45:3
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Other program demographic characteristics were also examined including program size (number
of clients served per year), program tenure, types of services provided, and specialization. As can
be seen from Table 2, programs that served less than 50 clients and those who served over 1000
clients annually had the highest mean heterophily scores (F (3, 65) = 3.5, p G .05). This effect
remained even when transition-age youth only programs were removed from the analysis (F (3,
52) = 2.7, p G .06), programs with over 1000 clients still had high heterophily (i.e., ≥ .50). This
suggests that the high scores in small programs were primarily due to overrepresentation of
transition-age youth only programs within small programs. Heterophily scores were not
significantly different between programs that had existed for different tenures with or without the
inclusion of transition-age youth only programs (F (2, 66) = 1.7, p 9 .10; F (2, 51) = 0.8, p 9 .10,
respectively).
With the exception of substance abuse services, there were no statistically significant
differences in heterophily scores between programs that offered different types of services (see
Table 2). Those programs that offered substance abuse services had lower heterophily scores than
those that did not offer substance abuse services (p G .05). When transition-age youth only
programs were removed from the analyses, there were no significant effects of service type on
heterophily scores. Program specialization, defined as the number of different types of services
offered (e.g., MH services plus vocational services = 2 types), did not significantly affect
Table 2
Mean heterophily scores by program demographic characteristics
Variable
Program size*
Less than 50 clients
51 to 199 clients
200–1000 clients
Over 1000 clients
Program tenure
0 to 10 years
11 to 20 years
over 20 years
Program specialization
1 service type
2 service types
3 service types
Services provided
Mental health
Housing
Independent living
Vocational
Substance abuse
Education
Recreation
Juvenile justice
All programs
Child only and adult only programs
X ± SD (n = )
X ± SD (n = )
.58 ± .28
.39 ± .20
.48 ± .28
.63 ± .19
.27 ± .22
.25 ± .20
.32 ± .29
.50 ± .25
(12)
(22)
(19)
(13)
(7)
(21)
(15)
(10)
.52 ± .27 (28)
.42 ± .22 (21)
.57 ± .23 (18)
.35 ± .28 (19)
.26 ± .19 (20)
.34 ± .30 (13)
.47 ± .26 (28)
.50 ± .21 (11)
.61 ± .24 (15)
Yes (n=)
.53 ± .26 (34)
.55 ± .22 (22)
.55 ± .27 (20)
.41 ± .28 (18)
.38 ± .27 (18)*
.56 ± .33 (15)
.44 ± .29 (12)
.46 ± .23 (9)
.30 ± .28 (22)
.29 ± .18 (9)
.43 ± .28 (11)
Yes (n=)
.33 ± .26 (28)
.33 ± .27 (15)
.24 ± .24 (13)
.22 ± .21(14)
.25 ± .27 (17)
.35 ± .37 (9)
.24 ± .20 (9)
.26 ± .25 (8)
No (n=)
.47 ± .24
.48 ± .26
.48 ± .24
.53 ± .23
.54 ± .23
.48 ± .22
.51 ± .24
.51 ± .26
(34)
(46)
(48)
(50)
(50)
(53)
(56)
(59)
No (n=)
.29 ± .25
.30 ± .25
.33 ± .26
.34 ± .26
.34 ± .25
.30 ± .23
.32 ± .26
.32 ± .26
(25)
(38)
(40)
(39)
(36)
(44)
(44)
(45)
*p G .05
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DAVIS ET AL.
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heterophily scores with or without transition-age youth only programs (F (3, 67) = 1.3, p 9 .10; F
(3, 52) = 1.3, p 9 .10, respectively, Table 2).
Programs’ Staff Members’ Perceptions Regarding Cross-Age Collaboration
Will Be Associated with Cross-Age Collaboration Rates
Table 3 presents the mean heterophily scores for those who agree versus disagree with
statements about collaboration between child and adult mental health. There were no significant
agree/disagree differences in mean heterophily scores on these six items when all programs were
included in the analysis. When transition-age youth only programs were excluded from analyses,
one statement (BMy program funders want to see more collaboration^) was significant. Those who
agreed with this statement had higher mean heterophily scores than those who disagreed with the
statement. The proportion of programs that agreed with each statement were quite similar whether
transition-age youth only programs were included or not.
Programs with Higher Levels of General Within- or Across-Program Mechanisms
of Collaboration Will Have Higher Rates of Cross-Age Collaboration
Scores for the intra-program and inter-program subscales of the MoCQ were not significantly
correlated with program heterophily scores ((N = 67), Pearson r = .09, p 9 .10; Pearson r = − .06,
p 9 .10, respectively). Removing transition-age youth only programs from the analyses did not
change these findings ((N = 53), Pearson r = − .02, p 9 .10, Pearson r = − .07, p 9 .10, respectively).
Discussion
The purpose of this study was to explore program characteristics and malleable program factors
associated with variability in cross-age collaboration levels within networks of services that
transition-age youth access. Of special interest was the extent to which programs serving only
children and programs serving only adults were engaging in cross-age collaboration. Our findings
regarding programs’ demographics, perceptions, and collaboration mechanisms revealed one
strong finding: compared to child only and adult only programs, programs that serve transition-age
youth only engaged in high levels of cross-age collaboration.
The high level of cross-age collaboration exhibited by programs that serve transition-age youth
only may be due to several factors. First, transition-age youth only programs may function as
specialized programs in their networks (i.e., serving a small population defined narrowly). Previous
literature on collaboration suggests that specialized organizations tend to be more limited in
resources and will refer clients to bigger and more generalized organizations as a way to best
benefit their clients.25, 26 Thus, the limited service capacity of transition-age youth only programs
may promote their collaboration with child only or adult only programs because those programs
have services or service capacity (e.g., openings in their services) that transition-age youth need. In
addition, these programs’ Bmiddle^ position, in terms of ages served, would also make cross-age
collaboration more likely. Child only programs would likely refer aging-out youth to these
programs, and these programs would likely refer those that age-out of their services to adult
programs. These referrals and activities associated with referral (such as meeting for client planning
purposes) likely lead to forging some relationships with both child only and adult only programs.
While the exact cause of the high heterophily scores among transition-age youth only programs
cannot be determined by the current study, it is clear that this group of programs is likely to have
developed skills for cross-age collaboration that are valuable to the system as a whole in enhancing
better collaboration between child only and adult only programs.
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*p G .05
There are significant barriers to coordinating across
child/adolescent and adult services in the system.
Think about 10 programs most important to your
program. Leadership from those 10 programs,
as a group, wants to see my program coordinate
better across child/adolescent and adult services
on behalf of transition-age youth and young adults.
Our funders want to see the programs they fund
coordinate more between child/adolescent and
adult services on behalf of transition-age youth
and young adults.
System leadership has developed ways for
child/adolescent and adult services to share
responsibility for transition-age youth and
young adults.
System leadership has set up accountability
mechanisms that require both child/adolescents
and adult program coordination in order to
achieve the targets.
System leadership rewards programs that have
coordinated well across child/adolescent and
adult systems.
Statement
Table 3
.51 ± .25
.52 ± .24
.50 ± .22
.56 ± .17
.48 ± .20
76
58
45
36
.51 ± .26
Agree
84
84
% agree
X ± SD
All programs
.51 ± .28
.46 ± .30
.50 ± .29
.45 ± .29
.46 ± .26
.47 ± .22
Disagree
43
38
− .4 (65)
58
− .1 (65)
1.6 (65)
77
83
85
% agree
0.9 (65)
0.6 (65)
0.4 (65)
t (df)
X ± SD
.32 ± .19
.38 ± .16
.33 ± .23
.35 ± .24
.33 ± .25
.32 ± .26
Agree
.32 ± .28
.27 ± .3
.30 ± .28
.19 ± .24
.24 ± .26
.29 ± .17
Disagree
− .02 (51)
1.5 (51)
0.4 (51)
2.0 (51)*
1.1 (51)
0.4 (51)
t (df)
Child only and adult only programs
Mean heterophily scores by agreement with statements about collaboration between adult only and child only programs
This research identified only one malleable program factor that was positively associated with
cross-age collaboration: the perception that funders wanted more cross-age collaboration among
the programs they fund. Among child only and adult only programs, this finding suggests that
funders could enhance cross-age collaboration through this mechanism. Given that none of the
other malleable factors were significantly correlated with heterophily scores, this mechanism is a
starting point for system reformers while other reliable mechanisms are yet to be discovered.
System reformers, thus, have two mechanisms that these findings suggest may enhance cross-age
collaboration: establish transition-age youth only programs and ensure that the child only and adult
only programs perceive that their program funders want to see more cross-age collaboration.
The only program marker of cross-age collaboration among child only and adult only programs was
the finding that substance abuse treatment providers were less likely to engage in cross-age
collaboration than those programs not providing substance abuse treatment. It may be that this type
of service has more concrete program requirements or higher levels of governmental regulation that
interferes with the flexibility that may be required to engage in cross-age collaboration. If substance
abuse treatment providers are to be integrated into the network of programs serving transition-age
youth, gaining a better understanding of the barriers to their cross-age collaboration would be valuable.
Limitations
Generally, the number of programs in networks of services that transition-age youth can access is
small, as was reflected in the size of the networks the current program participants were recruited
from. When categorized by the ages served by the program, the number of programs in each
category became even smaller. While the sample size for this study was underpowered to detect
weak effects, the 20% missing response rate had a small to negligible effect on examining the
overall network, as many of the missing agencies were peripheral to the network.44–46 A second
limitation comes from the lack of prior research into relationships among programs that serve
transition-age youth. To date, there has been no metric established to measure the level of cross-age
collaboration that exists within a service system. The work presented here is one of the first efforts
to use data generated by social network analysis to measure this construct. One of the challenges in
this new research area is that there is no commonly accepted way of measuring cross-age
collaboration that can be used for validation. Without a separate Bgold standard^ that establishes
what a Bgood^ or Bweak^ heterophily score is, researchers are dependent on face validity of the
measure to prove its worth and are limited to simply describing higher or lower levels without any
guidelines about concerning or strong levels.
Heterophily, as an index, makes sense as a good measure of cross-age collaboration but whether
it will become useful in making decisions about programs depends on validating it further,
establishing guidelines for its valence, and understanding how programs with strong and weak
heterophily function in networks. Because few of the variables demonstrated a significant
relationship to heterophily scores, the causes of higher or lower heterophily are still unexplained.
While these findings demonstrated higher levels of heterophily in transition-age youth only
programs, the study does not elucidate why these programs had high heterophily scores, leaving
this question to future research.
Implications for Behavioral Health
As the motivation to and knowledge about how to better serve transition-age youth with mental
health conditions has grown, so too has the awareness of difficulties erected by the age divide between
child and adult services. One way to help remedy those barriers is by better linking programs that serve
these different age groups. Collaborations among programs foster innovation and the exchange of
knowledge about the other services, as well as improving services for transition-aged youth. Thus, the
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45:3
July 2018
ability to measure cross-age collaboration accurately and the ability to increase cross-age collaboration
in some programs are critical factors in the planning and administration of mental health services for
this population. Programs that serve transition-age youth may naturally be inclined to collaborate across
the age barrier because it is clear that their clients need the services and supports available in the adult or
child system. However, planners and administrators need strategies to encourage programs that serve
children only or adults only to collaborate across the age divide. To encourage cross-age collaboration
in these programs, managers may need to create external incentives or pressures. The present findings
suggest that one such incentive is for funders to clearly communicate the expectation for cross-age
collaboration. This could be achieved through mechanisms such as contract language or RFA criteria
that require cross-age collaboration. Other approaches might include financial incentives, access to
special consultation or training resources, or special recognition within the service delivery system.
Finally, research that explores additional program factors that can be modified is needed in order to
understand how to increase cross-age collaboration. For example, staff training that focuses specifically
on services within the adult mental health system and how to access these services might increase crossage collaboration from both the child only and transition-age youth only programs. The research
reported here represents a beginning step toward measuring cross-age collaboration and thinking about
programmatic interventions that will support better services for transition-age youth.
Acknowledgements
This manuscript was developed under a grant with funding from the National Institute on
Disability, Independent Living, and Rehabilitation Research and the Center for Mental Health
Services of the Substance Abuse and Mental Health Services Administration (Grant
H133B090018, to the first author, The Learning and Working During the Transition to Adulthood
RRTC). We are grateful to John Coppola, Pnina Goldfarb, Bruce Kamradt, and DeDe Sieler for
their help with this project and the programs and their respondents who participated in this
research. The contents of this paper do not necessarily represent the policy of NIDILRR or
SAMHSA and you should not assume endorsement by the Federal Government.
Compliance with Ethical Standards
Conflict of Interest
There are no conflicts of interest to report.
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