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Sarah Gehlert, Bobbi J. Carothers, Jung Ae Lee, Jefferson Gill, Douglas Luke, Graham Colditz
A Social Network Analysis Approach to Diagnosing and Improving the Functioning of Transdisciplinary Teams in Public
Health 1
A Social Network Analysis
Approach to Diagnosing and
Improving the Functioning of
Transdisciplinary Teams in Public
Health
Sarah Gehlert, PhD1,2Bobbi J. Carothers, PhD3Jung Ae Lee, PhD5Jefferson Gill, PhD4
Douglas Luke, PhD1,3Graham Colditz, MD, PhD2,5
1The George Warren Brown School of Social Work, Washington University, St. Louis, MO, USA, Email: sgehlert@wustl.edu;
2The Siteman Cancer Center, Washington University, St. Louis, MO, USA; 3Center for Public Health Systems Science,
Washington University, St. Louis, MO, USA; 4Department of Political Science, Washington University, St. Louis, MO, USA;
5Division of Public Health Sciences, Department of Surgery, Washington University, St. Louis, MO, USA
B
ackground: The National Cancer Institute’s
Transdisciplinary Research in Energetics and
Cancer initiative is in its second round of
funding. Despite increasing agreement that trans-
disciplinary team-based research is valuable in ad-
dressing complex problems like energy balance and
cancer, methods for constructing and maintaining
transdisciplinary teams is lacking.
Purpose: We articulate a method for assessing trans-
disciplinary teams that relies on social network anal-
ysis and using this knowledge to improve their func-
tioning.
Methods: Using data from the Washington Univer-
sity TREC site in 2011 and 2013, we demonstrate
the use of social network analysis to assess and pro-
vide feedback on team functioning.
Results: We portray broker functioning in both years.
By 2013, the director and co-director had begun to
share broker functions with other members. Some
brokers fostered communication with less central net-
work members.
Conclusions: The information obtained can help to
train a new generation of investigators to optimally
participate on transdisciplinary research teams.
Keywords
:transdisciplinary research team, social
network analysis, transdisciplinary assessment.
1 Introduction
Transdisciplinary research gained attention in
medicine in the 1990s and early 2000s, when the Na-
tional Cancer Institute mandated use of the approach
in funding initiatives aimed at addressing tobacco
use, disparities, and energy balance in cancer. The
Transdisciplinary Tobacco Use Research Centers,
Centers for Population Health and Health Dispari-
ties, and Transdisciplinary Research in Energetics
and Cancer were funded in 1997, 2003, and 2005,
respectively. The impetus for their development was
a desire to capture the complexity of phenomena
Transdisciplinary Journal of Engineering & Science
ISSN: 1949-0569 online, c
2014 TheATLAS
Vol. 6, pp. 1-12, (December, 2015)
Sarah Gehlert, Bobbi J. Carothers, Jung Ae Lee, Jefferson Gill, Douglas Luke, Graham Colditz
A Social Network Analysis Approach to Diagnosing and Improving the Functioning of Transdisciplinary Teams in Public
Health 2
like cancer disparities using teams of disciplinary
scholars that spanned the social, behavioral, and
biological sciences to provide an integrated, holistic
approach.
Transdisciplinary research differs from multidisci-
plinary and interdisciplinary research in the extent
to which investigators operate outside the bound-
aries of their own disciplines to share language, pool
knowledge and theories, and develop new methods
of analysis. In multidisciplinary research, investi-
gators come together to solve a research problem,
but approach it through separate disciplinary lenses.
They might, for example, gather at the beginning of
a research project with separate but related research
questions, collect and analyze data independently,
form independent conclusions based upon their sep-
arate research questions, and come together at the
end of the project to try to make sense of it all. Very
rarely do conclusions derived from such a multidisci-
plinary approach fit together neatly into a coherent
whole, and investigators exit the collaboration with
no discernible change in their approaches to science.
Investigators working interdisciplinarily transfer dis-
ciplinary knowledge to one another for the purposes
of research, and may share research questions, yet
resurrect their disciplinary boundaries when an an-
swer has been found that serves the needs of their
root disciplines.
Transdisciplinary research, which Rosenfield de-
fines as exchanging information, altering discipline-
specific approaches, sharing resources, and integrat-
ing disciplines to achieve a common scientific goal
[1], represents the highest degree of disciplinary col-
laboration. Hall et al. describe its four phases as:
(1) development, in which a transdisciplinary team
is assembled; (2) conceptualization, during which
research questions are refined; (3) implementation,
which involves negotiating shared models and goals;
and, (4) translation, through which discoveries are
translated into change [2]. In this approach, disci-
plinary scholars transcend and operate outside their
own boundaries to achieve synergy, mutually-inform
one another’s work, and create a new intellectual
space in which no one discipline dominates and no
way of knowing is privileged over others. The ap-
proach has the potential to forge new understandings
of major public health problems by breaking down
the usual barriers to shared scholarship. Emmons
et al. use energetics and cancer as an example of
the tremendous inefficiency that occurs when bound-
aries between the social, behavioral, and biological
are rigidly maintained in research, saying “If the
primary focus of work in obesity and energy balance
is on sociocultural factors, eventually the limits of
not considering both environmental and physiologic
factors will be realized [3, p. S205].”
2 The Transdisciplinary Research
on Energetics and Cancer
Initiative
The mission of the National Cancer Institute’s Trans-
disciplinary Research on Energetics and Cancer
(TREC) initiative, which includes multiple sites
around the country, is to foster collaboration across
multiple disciplines and projects to cover the spec-
trum from the biology, genetics, and genomics of
energy balance to social and behavioral influences
on physical activity and nutrition, weight, energetic,
and cancer risk [4]. This collaboration is meant to
occur within and across TREC sites. Currently in
its second five-year round of funding, the four TREC
sites are the University of Pennsylvania, Washington
University, the University of California-San Diego,
and Harvard University, with a Coordination Center
at the Fred Hutchinson Cancer Research Center in
Seattle. The TRECs also have a mission to train
new and established investigators to carry out inte-
grative research on energetics and energy balance
and cancer.
Each TREC has approximately four research
projects and a number of cores to support their
work. In addition, the initiative funds a number
of within- and cross-TREC developmental research
projects that are selected from applications solicited
once a year by the TREC Steering Committee, which
is made up of the principal and co-principal inves-
tigators of the TREC sites and the Coordination
Center. The developmental projects are meant to
extend the research of the TRECs into new areas of
discovery and translation.
3 Evaluating Effectiveness within
and Across TREC Sites
Considering the group of TREC investigators as a
social network, or a social entity made up of a num-
ber of actors, allows the group’s functioning to be
Transdisciplinary Journal of Engineering & Science
ISSN: 1949-0569 online, c
2014 TheATLAS
Vol. 6, pp. 1-12, (December, 2015)
Sarah Gehlert, Bobbi J. Carothers, Jung Ae Lee, Jefferson Gill, Douglas Luke, Graham Colditz
A Social Network Analysis Approach to Diagnosing and Improving the Functioning of Transdisciplinary Teams in Public
Health 3
analyzed in its entirety as well as the dyadic rela-
tionships between its members. The assumption
is that stronger and more frequent communication
and the formation of new cross-disciplinary ties will
better foster advances in the science of energy bal-
ance and cancer. It is assumed that investigators
will at minimum be aware of the mission of their
TREC site and of the broad objectives of its re-
search projects. Optimally, they would be conver-
sant in one another’s work through exposure during
regularly-scheduled team meetings and through the
co-mentoring of trainees [5]. At the Washington Uni-
versity TREC site, for example, postdoctoral fellows
select three mentors that span disciplines from a
menu of faculty who have agreed to participate in
the training program.
Investigators might communicate directly with
one another, as in the case of project and core lead-
ers, or through members of the team who serve as
brokers of information about the TREC site as a
whole and its shared research agenda. Brokers are
defined as network members who link to otherwise
unconnected members of the network [6]. They play
important roles in linking members by serving as
go-betweens in terms of information transfer and
day-to-day communication of research activities and
findings.
Brokers might be the principal mode of commu-
nication for investigators who are less centrally in-
volved in projects and cores. Inevitably, some re-
searchers are less involved than others, especially
those whose academic responsibilities are less-well
covered by TREC funding. A challenge of transdis-
ciplinary sites is to engage investigators who are less
central to the TREC funding mechanism and those
who might be dispersed due to off-site clinical du-
ties. Bringing less well-integrated investigators into
communication with the team as a whole ensures
the maximization of TREC’s scientific discovery and
translation. One example of how this might occur
is when a broker is able to convey information and
ideas about potential cross-site or cross-project devel-
opmental projects to other members of the network.
More ties between investigators signify greater
communication within a network. The principal
way of evaluating this quality is by measuring the
density of social network ties, defined as the number
of actual ties between network members compared
to the number of potential ties. Denser networks
suggest faster propagation of information and greater
group cohesion [6]. Also, individuals who conduct
more information tend to be more productive in
terms of research goals and objectives [7].
Despite increasing agreement that transdisci-
plinary research is a valuable approach to addressing
complex problems like energy balance and cancer,
methods for constructing and maintaining effective
transdisciplinary teams are lacking. In the present ar-
ticle, we articulate a method for diagnosing transdis-
ciplinary teams’ strengths and weaknesses that relies
on social network analysis, and using this knowledge
to improve their functioning. With data from the
Washington University Transdisciplinary Center for
Energetics and Cancer at two time points (2011 and
2013), we demonstrate how social network analysis
was used to assess and provide actionable feedback
on the strengths and weaknesses of the team. We
then discuss how the information obtained might be
used to train a new generation of investigators to
optimally participate on transdisciplinary research
teams.
4 Methods
4.1 Study Sample
Participants in the study were investigators involved
in the TREC research site at Washington University
in St. Louis, including postdoctoral trainees. Here
we report on 24, 31, and 31 investigators from Wash-
ington University who were involved in the social
network analysis research project in 2011, 2013 and
2014, respectively.
A list of investigators was developed by the TREC
Steering Committee, and after receiving IRB ap-
proval, each was sent a letter inviting them to partic-
ipate along with a copy of the social network survey.
The survey was sent during the first months of the
second round of funding for the TREC sites, for the
purpose of establishing a baseline measure of ties.
None of the sites funded during the first round of
TREC funding was refunded, thus all sites were new
to TREC. The survey will be re-sent yearly through-
out the five years of funding to assess the increase
in density of social network ties over time. In the
current paper, we report on data from the first three
years of data collection, using data from the Wash-
ington University site only. Note that the number of
respondents for the survey is less than the network
size, or the total members in the network (see Table
Transdisciplinary Journal of Engineering & Science
ISSN: 1949-0569 online, c
2014 TheATLAS
Vol. 6, pp. 1-12, (December, 2015)
Sarah Gehlert, Bobbi J. Carothers, Jung Ae Lee, Jefferson Gill, Douglas Luke, Graham Colditz
A Social Network Analysis Approach to Diagnosing and Improving the Functioning of Transdisciplinary Teams in Public
Health 4
Table 1: Response Rate and Network Size for the Washington
University TREC site in 2011, 2013 and 2014
2011 2013 2014
Number of invited 25 25 22
Number of respondents 20(80%) 20(80%) 19(86%)
Professor 998
Associate Professor 143
Assistant Professor 655
Research Associate 211
Other 212
Network size (N) 24 31 31
Table 2: Nature of Collaboration for the Washington University TREC site in 2011, 2013
and 2014 (unit: % of the total ties in each year)
Year Grant Co-authored Co-authored Mentorship Committee Others
Publication Presentation
2011 37.66 18.18 11.69 31.17 35.71 3.9
2013 62.5 28.8 17.93 30.43 36.96 5.98
2014 43.53 42.94 28.82 38.82 38.82 7.06
1). The number of survey respondents, who are the
primary actors in the network, is 20, 20, and 19 in
2011, 2013, and 2014, respectively. The difference
between the respondents and the total network ac-
tors is due to the secondary actors who are included
in the network because they were designated by re-
spondents as a link but did not themselves respond
to the survey. Thus we do not know about the re-
lationship among the secondary actors. We address
this because the network density can be sensitive to
the number of respondents rather than the network
size, which often makes it hard to fairly compare the
longitudinal social network data due to different set
of respondents each time point.
5 Measures
5.1 Collaboration Network
The survey included the names of all TREC investiga-
tors and asked respondents if they currently worked
with, or had worked with prior to TREC, any inves-
tigators on the list in any of six situations:
1. on a study or grant;
2. on a co-authored publication;
3. on a co-authored presentation;
4. in mentoring or training;
5. on a committee or work group; or,
6. in any other activity.
For the purposes of the present study, partici-
pants were considered to have worked with another
investigator if they indicated that they currently
were collaborating on any of the six listed activities.
The percent of collaboration types is summarized in
Table 2.
5.2 Discipline
Investigators were asked to choose from a list of 37
academic disciplines the one which best character-
ized the primary disciplinary perspective of their
Transdisciplinary Journal of Engineering & Science
ISSN: 1949-0569 online, c
2014 TheATLAS
Vol. 6, pp. 1-12, (December, 2015)
Sarah Gehlert, Bobbi J. Carothers, Jung Ae Lee, Jefferson Gill, Douglas Luke, Graham Colditz
A Social Network Analysis Approach to Diagnosing and Improving the Functioning of Transdisciplinary Teams in Public
Health 5
Table 3: Number of Investigators by Disciplines for the
Washington University TREC site in 2011,
2013 and 2014
Categories of Disciplines 2011 2013 2014
2. Statistics & System 4 4 4
3. Social/Behavioral Science 5 6 6
4. Epidemiology 4 5 5
6. Medicine 8 10 10
7. Nutrition/Metabolism 1 3 3
8. Public Health Practice 2 3 3
Total 24 31 31
Table 4: Network Density for the Washington University TREC site in 2011, 2013 and
2014
Network No. All No. No. Density
size Respondents possible edges edges triangles
2011 24 20 276 72 79 0.26
2013 31 20 465 109 158 0.23
2014 31 19 465 96 119 0.21
2014b 31 23 465 104 148 0.22
work. For purposes of analysis, these responses were
collapsed into eight disciplines (see Table 3).
5.3 Density
Density is the proportion of ties that exist among
all possible ties (also called edges). From Table 4,
we can see the density remains the same or slightly
decreases over time. It is worth noting, however,
that the density measure is sensitive to the growth
of network size and the change of respondents. As
a network gets larger, the density tends to decrease
since the number of possible edges is
n2−n
2
and this
grows faster proportionally than the number of re-
lationships. Also, losing one member, especially if
that person is an active collaborator may result in
losing all of that member’s related ties, thus reduc-
ing overall density. For this reason, it is prudent to
look at the absolute number of edges in the network
(Table 4). We see the network expanded the most in
2013, with 109 ties compared to 72 ties in 2011 and
96 ties in 2014. The active collaboration in 2013 is
partly because there was considerable grant related
work in 2013 (Table 2). Although the overall den-
sity decreased in 2014 compared to previous years
(Table 4), the collaborative work represented by the
number of co-authored publications and presenta-
tions increased in 2014 (Table 2). In addition, we
attempted to recover 2014’s missing data for the
respondents who responded the survey in 2013 but
did not response in 2014 by assuming that the net-
work relations of these members remained the same
through 2013-14. We add this missing imputation
result in 2014b in Table 4. The improved number of
edges in 2013 and 2014 (2014b) compared to 2011
indicates general improvement of collaborative func-
tions within the Washington University TREC site.
We further investigate how the characteristics of
network transform over time.
6 Analysis
Social network analysis was used as the principal
mode of analysis in the present study. We consid-
ered the investigators of the Washington University
TREC site who participated in our survey as the
Transdisciplinary Journal of Engineering & Science
ISSN: 1949-0569 online, c
2014 TheATLAS
Vol. 6, pp. 1-12, (December, 2015)
Sarah Gehlert, Bobbi J. Carothers, Jung Ae Lee, Jefferson Gill, Douglas Luke, Graham Colditz
A Social Network Analysis Approach to Diagnosing and Improving the Functioning of Transdisciplinary Teams in Public
Health 6
Figure 1:
Illustration of the Five Brokerage Types in Directed Network. Colored dots are actors where the top
point is the broker. Ellipses are the discipline boundaries.
network actors in our analyses. We define brokers as
individuals who collaborate with two other network
members who do not collaborate with one another,
and assume mutual rather than unidimensional col-
laboration.
Gould and Fernandez [8] define brokerage relations
based on ordered triples of actors in a sequence
of communication. A coordinator broker connects
two members of the same discipline of which the
broker is also a member, while a consultant broker
connects two members from the same discipline of
which the broker is not a member (see Figure 1). A
liaison broker connects two network members from
different disciplines and is not a member of either of
those disciplines. A representative broker connects a
member of the broker’s own discipline to a network
member from another discipline. The gatekeeper
broker involves the same three types of members (i.e.,
the broker and one member each from the same and a
different discipline) as in a representative brokerage,
but differs in the direction of information flow. The
gatekeeper broker controls the flow of information
from the outside-discipline member to the member of
the gatekeeper’s own discipline. In other words, the
gatekeeper broker decides whether to grant access to
a member from his own discipline, which effectively
would cut that member off from network information.
Because a representative and a gatekeeper broker
are indistinguishable from one another in a mutual
network [6, 9], we merged them into one category,
which we designated as representative/gatekeeper.
For any ordered triple of actors
i, j
and
k
, the
situation in which
j
is a broker between
i
and
k
is denoted by the symbol
ijk
. In other words,
ijk
means
i
is tied directly to
j, j
is tied directly to
k
,
and
i
is not tied directly to
k
. Then, an actor
j
’s
total brokerage activity in a network with N actors
is defined by measuring the number of ordered pairs
(
i, k
) in the network for which the condition
ijk
holds. We modify the brokerage measure by a set of
subgroups. The first type is denoted by
mi
=
mj
=
mk
since all three actors belong to the same group.
Similarly, the second type of relationship is described
by
mi
=
mk6
=
mj
showing that two endpoints (
i, k
)
belong to the same group while the broker
j
is an
outsider, and so forth in other types. Then, an actor
j
’s coordinator brokerage score, named as
wIj
, is
defined as follows:
wIj =
N
X
i
N
X
k
wI(ik),(i6=j6=k),
where N is the number of actors in the network, and
wI
(
ik
) equals 1 if
ijk
is true and if
mi
=
mj
=
mk
, and 0 otherwise. The other subtypes of broker-
age can be computed following the same principle
and named as
wOj , bI Oj , bOI j , boj
, corresponding to
consultant, representative, gatekeeper, and liaison,
respectively. The notation
w
is used for the coordina-
tor and consultant brokerage score denoting within
group roles because two endpoints (
i, k
) belong to
the same subgroup. On the other hand, the repre-
sentative, gatekeeper, and liaison brokerage scores
are notated by
b
because these are between group
brokerage roles with two endpoints belonging to the
different subgroups. Consequently, any actor
j
’s to-
tal raw brokerage score
tj
can be calculated by the
summation of the raw scores from these five sub-
types. This raw measure is useful if the researcher
is interested in the number of brokerage relations
that an actor is capable of mediating. On the other
hand, when the central issue is the degree to which
the actor actually controls brokerage relations in the
Transdisciplinary Journal of Engineering & Science
ISSN: 1949-0569 online, c
2014 TheATLAS
Vol. 6, pp. 1-12, (December, 2015)
Sarah Gehlert, Bobbi J. Carothers, Jung Ae Lee, Jefferson Gill, Douglas Luke, Graham Colditz
A Social Network Analysis Approach to Diagnosing and Improving the Functioning of Transdisciplinary Teams in Public
Health 7
Table 5: Brokers by a Criterion that Standardized Brokerage Scores >1.64 for the Washington
University TREC site in 2011, 2013 and 2014
Investigator Id
Brokerage Relations 2011 (N=24) 2013 (N=31) 2014 (N=31)
1. Coordinator 17, 21, 23 1, 5, 6, 17, 21, 31 5, 17, 21, 31
2. Consultant 17, 21 17, 20, 21 17, 21, 28
3. Liaison 17, 21 17, 21 17, 21, 28
4. Representative/Gatekeeper 17, 21 1, 17, 21, 31 1, 17, 21, 31
network, then the appropriate measure is the partial
score
t∗
j
, which can be computed analogous to
tj
but
divided by gik. For example,
w∗
Ij =
N
X
i
N
X
k
wI(ik)/gik ,(i6=j6=k, gik 6= 0),
In which
gik
is the number of two-step paths be-
tween
i
and
k
. Our analysis used
t∗
j
because our
interest lies in the degree of each actor’s contribu-
tion as a broker in the network rather than his or her
capacity as a broker. The network as a whole can
be characterized in the same terms. This is referred
to as a global brokerage measure of the network and
defined as
WI=
N
X
j
WIj .
We computed a standardized brokerage score for
each Washington University site network member,
which enables us to directly compare brokerage
scores across the years [6]. The standardized scores
(
β
) are computed by
β
= (
b−µb
)
/σb
, where
b
is
any of the brokerage scores defined above,
µb
is the
expected value of
b
and
σb
is the standard deviation
of
b
under the null model. It is reasonably assumed
that the standardized brokerage scores of actors fol-
low the standard normal distribution for sufficiently
large networks (about 15 actors for global scores,
30 for individual scores) [8]. Thus a higher positive
score for an actor means that the actor occupies
more brokerage positions than actors with lower bro-
kerage scores. A negative extreme score, however,
means that the actor avoids being a mediator in the
network or prefers to operate independently. We
used a 0.1 significance level, which further helped
us to determine whether or not a network member
was a broker. Our criterion for deciding if a network
member was a broker was whether the investigator’s
standardized brokerage score was greater than 1.64,
the approximate value of the 95th percentile point
of the standard normal distribution. The brokerage
scores were calculated in the same way for each type
of brokerage. We used the R package ‘sna’ [10] to
derive functions for all brokerage scores.
In principle, we reject the null hypothesis of a
random network when we observe too many or small
number of actors at extremes (
>
1.64 or
<
-1.64).
This is done by comparing the number of members
whose scores fall at the ends of the distribution to
what we would expect in a random network. For
example, in a completely random network with 31 ac-
tors, only 5 percent of the actors (1.6 actors) should
have a high positive brokerage score (>1.64).
7 Results
We observed more Washington University TREC site
network members in 2013 and 2014 with brokerage
scores that were higher than 1.64 (i.e., higher than ex-
pected), which led us to reject the null hypothesis for
2013 and 2014 (see Table 5). We could not, however,
reject the null hypothesis for 2011. We took this to
mean that while the Washington University TREC
network was not particularly brokerage-oriented in
2011, it had become so by 2013 and 2014. This obser-
vation is consistent with the global measure in Table
6. This table displays global measures of brokerage
for all brokerage types and for brokerage in general.
The 2013 total score is statistically significant at the
0.05 level (score
>
1.96). By 2013, pairs of actors
were more likely to be brokered than they were in
2011 or than they would be in a random network.
Transdisciplinary Journal of Engineering & Science
ISSN: 1949-0569 online, c
2014 TheATLAS
Vol. 6, pp. 1-12, (December, 2015)
Sarah Gehlert, Bobbi J. Carothers, Jung Ae Lee, Jefferson Gill, Douglas Luke, Graham Colditz
A Social Network Analysis Approach to Diagnosing and Improving the Functioning of Transdisciplinary Teams in Public
Health 8
Table 6: Global Brokerage Measures for the Washington
University TREC site in 2011, 2013 and 2014
Standardized Global
Brokerage Score
Brokerage Relations 2011 2013 2014
1. Coordinator 1.46 1.57 1.35
2. Consultant -0.71 0.76 0.13
3. Liaison 1.1 1.85 0.45
4. Representative/Gatekeeper 1.23 1.58 2.28
Total 1.26 2.2 1.49
Table 7: Standardized Brokerage Scores for Principal Investigator (PI) and
Co-principal Investigator (Co-PI) for the Washington University TREC site
in 2011, 2013 and 2014
PI Co-PI
Brokerage Relations 2011 2013 2014 2011 2013 2014
1. Coordinator 6.06 6.3 7.2 7.72 10.75 8.58
2. Consultant 16.64 6.0 11.68 3.89 25.54 11.61
3. Liaison 24.06 9.85 14.64 10.08 34.88 18.62
4. Representative/Gatekeeper 11.88 5.81 7.08 9.58 18.65 15.58
Total 25.78 10.98 16.09 12.99 37.96 23.28
The liaison role’s relatively high global score (1.85)
in 2013 suggests that actors in the system are more
likely to participate in brokerage activities in which
all belong to different disciplines. The high liaison
score of the global measure is also related to the
high liaison roles of the PI and Co-PI (Table 7). The
2014 global brokerage score is statistically significant,
especially for representative/gatekeeper brokerage
(2.28). From Table 5, we see that the members 1
and 31 contribute to this brokerage role in addition
to the PI and Co-PI.
Figure 2 portrays the Washington University
TREC site network, with nodes sized according to
total brokerage score and colored according to disci-
plinary category. In 2011, the principal investigator
(PI), an epidemiologist, and the co-principal inves-
tigator (Co-PI), a social scientist, were the two pri-
mary brokers in the Washington University TREC
network. By 2013, more brokerage relations are
observed among other network members. Table 5
lists broker relations for each year according to a
criterion of
>
1.64. For example, member 1 acted
as a coordinator and representative/gatekeeper for
the discipline of Medicine in 2013. In 2014, this
brokerage role is more solidified for some members:
member 1 remains as a representative/gatekeeper,
member 28 is a consultant and liaison, and member
31 is a coordinator and representative/gatekeeper.
Standardized brokerage scores for the PI and Co-
PI for all brokerage types can be found in Table
7. In general, both the PI and Co-PI have higher
brokerage scores than other network members across
all four brokerage types, indicating that their inter-
mediary roles as a coordinator, consultant, liaison
and representative/gatekeeper are all crucial in this
network. Their roles as liaisons are especially salient
in the network, linking all disciplinary categories. It
is also the case that the broker relations of the PI
and Co-PI changed through the years. The Co-PI’s
broker relations were substantially higher in 2013
than 2011, as seen by the higher total brokerage
score for the latter year (see Table 7), and decreased
Transdisciplinary Journal of Engineering & Science
ISSN: 1949-0569 online, c
2014 TheATLAS
Vol. 6, pp. 1-12, (December, 2015)
Sarah Gehlert, Bobbi J. Carothers, Jung Ae Lee, Jefferson Gill, Douglas Luke, Graham Colditz
A Social Network Analysis Approach to Diagnosing and Improving the Functioning of Transdisciplinary Teams in Public
Health 9
Figure 2:
Network Plot of the Washington University TREC site in 2011, 2013 and 2014. The number in nodes is
the unique id of investigators, and the node size indicates the total brokerage score of the investigator.
The nodes are colored by different disciplines. The nodes of 17 and 21 are downsized than actual
brokerage score for visual convenience. In 2011, PI and Co-PI are the main brokers in this network. In
2013 and 2014, more brokerage relations exist (see also Table 5).
Transdisciplinary Journal of Engineering & Science
ISSN: 1949-0569 online, c
2014 TheATLAS
Vol. 6, pp. 1-12, (December, 2015)
Sarah Gehlert, Bobbi J. Carothers, Jung Ae Lee, Jefferson Gill, Douglas Luke, Graham Colditz
A Social Network Analysis Approach to Diagnosing and Improving the Functioning of Transdisciplinary Teams in Public
Health 10
a bit in 2014. Meanwhile, the PI tended to distribute
his brokerage role to other members by 2013-14, evi-
denced by a lower total brokerage score in 2013-14
than 2011 and the increased numbers of brokers in
2013-14 (see Table 5). It is worth noting that a de-
crease in brokerage score does not necessarily mean
a decrease in collaborative relations. It also can
indicate an increase in triangle relations (complete
transitivity).
Another natural way to measure brokerage is
through the centrality measure known as between-
ness [11],
g
(
w
) =
Ps6=w6=tσst(w)
σst
, where
σst
(
w
) is the
number of “paths” from node
s
to node
t
that pass
through node
w
and
σst
is the number of shortest
paths between them. We computed the betweenness
for all investigators and found that the correlation
between the betweenness and the brokerage score
was extremely high (0.99) in all years. In fact, be-
tweenness and brokerage scores are similar in their
abilities to measure the extent to which each actor
controls the network as a broker. The difference is
that betweenness is based on counting the shortest
paths of a pair (i, k) that the actor jlies on, which
can include more than two-steps relations, whereas
the total brokerage score only regards a two steps
brokerage process. Thus it is not surprising two
measures are empirically close in a small network
like ours. We include both, with the knowledge
that the brokerage score provides a more precise
measure of the kinds of brokerage and clarifies the
characteristics that each actor may perform.
Representative brokers are in a better position to
link people who might otherwise not link with one
another as a result of being in the same discipline.
These individuals may therefore be more transla-
tional in their research. It is interesting to note that
in all years, network investigators from the medical
discipline on the whole had the fewest network ties.
Yet, when the representative brokerages were con-
sidered, it became clear that one network member
from medicine linked the medical investigators to
other members of the network. Coordinators, who
broker relationships in which all members are from
the same discipline, arguably may be seen as less
translational. We intend to follow this pattern over
time to see if it is replicated.
8 Discussion
A number of findings from our use of social network
analysis to assess and provide actionable feedback on
the functioning of the Washington University TREC
team suggest ways in which the transdisciplinary
functioning of the group can be improved. Under-
standing the patterns and types of brokers within the
TREC social network allows plans to be developed
for increasing the connectedness of investigators, es-
pecially those who have not been well integrated into
the network. This raises the likelihood that their
perspectives will add to the team’s ability to capture
the complexity of energy balance and cancer.
Identifying who serves as liaison brokers suggests
ways of fostering engagement through communica-
tion and training. Likewise, identifying consultant
brokers suggests a way of linking members of the
same discipline within the network who previously
were unlinked. Information on coordinator brokers
suggests another way of organizing disciplinary sub-
networks within the network, with the goal of better
integrating them into the network. Similarly, iden-
tifying representative brokers provides a first step
toward connecting disciplinary sub-networks of dis-
ciplines with the network as a whole.
We found that investigators from the medical dis-
cipline had the fewest ties to the TREC network,
which was rendered less worrisome when we deter-
mined that they were being linked to the network
through one network member from medicine who
leads one the TREC’s four research projects (see
Figure 2). This investigator plays a valuable role in
transmitting information and ideas between medical
discipline investigators and other network members.
We also found that although the PI initially ful-
filled the most brokerage functions, by the second
time of data collection, he had begun to share these
functions with the Co-PI and other network mem-
bers, thus minimizing burnout and helping to ensure
the flow of communication among TREC site inves-
tigators. That the two administrators of the site
held complementary roles in terms of integrating
investigators within the site maximized communi-
cation and helped to ensure that information was
flowing, thus optimizing the development of new re-
search questions, methods, and analyses that mark
the success of transdisciplinary research.
Transdisciplinary Journal of Engineering & Science
ISSN: 1949-0569 online, c
2014 TheATLAS
Vol. 6, pp. 1-12, (December, 2015)
Sarah Gehlert, Bobbi J. Carothers, Jung Ae Lee, Jefferson Gill, Douglas Luke, Graham Colditz
A Social Network Analysis Approach to Diagnosing and Improving the Functioning of Transdisciplinary Teams in Public
Health 11
8.1 Implications for Process and Training
The pattern of brokerage across the site suggests
areas of focus to ensure optimal transdisciplinary
communication while conserving valuable resources,
not the least of which is investigator time. This
occurs by virtue of providing information to help ad-
ministrators determine the frequency and structure
of team meetings in a way that balances the benefits
of the exposure to ideas from the full range of disci-
plines with the cost of scheduling and implementing
meetings. For example, we now know the benefit of
including our representative broker from medicine
in wider team meetings, because her ties to other
network members from medicine will help to ensure
that they receive information from other disciplines.
Likewise, analysis of brokerages suggests how to
optimize team functioning by training investigators
to assume certain broker roles. In our example, med-
ical investigator team members were less likely to
be engaged with members from other disciplines,
perhaps because of their clinical duties and phys-
ical location at sites outside the medical campus.
Our social network analysis let us realize the impor-
tance and viability of having a medical investigator
serve as a representative broker role. Had one not
been available to serve the role, it would have been
prudent to train someone to assume that position.
Other brokers, such as liaison brokers, connect in-
vestigators from outside their own discipline with
members from other disciplines, in other words, they
foster transdisciplinary groupings of investigators.
This is essential for achieving the benefits of transdis-
ciplinary research. Network analysis allows a team
to determine whether the broker function is being
filled and if so, how successfully. If neither adminis-
trators nor other investigators are assuming the role,
a member or members can be trained in the liaison
broker role.
We report only for the Washington University
TREC in the present paper, in order to investigate
one network in depth. We will report on data from all
five TREC sites (i.e., the University of Pennsylvania,
the University of California-San Diego, Washington
University, Harvard University, and the Coordina-
tion Center at the Fred Hutchinson Cancer Research
Center) for the years reported in the present paper
and for 2015. It is possible that the picture seen for
Washington University will change when data from
other sites are included and another year of data is
added.
9 Conclusions
Transdisciplinarity and team science have increas-
ingly been recognized for their benefit in advancing
new scientific discoveries and moving those discover-
ies to translation into patient care and community
health. Yet, how to achieve the benefits of trans-
disciplinary research is neither intuitive nor based
upon clear guidelines from funders. Social network
analysis that considers broker functions on research
teams provides a means of assessing team function-
ing so that changes can be implemented to maximize
functioning. These changes, based on the flow of in-
formation, allow mid-course corrections to be made
rather than second guessing when teams are not
functioning as planned.
Acknowledgments
This research was supported by an NCI grant U54
CA155496.
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About the Authors
Sarah Gehlert, PhD
is the E. Desmond Lee Professor of
Racial and Ethnic Diversity at the George Warren Brown
School of Social Work and Professor in the Department
of Surgery at Washington University in St. Louis. Dr.
Gehlert is the Associate Director of the U54 Washington
University Transdisciplinary Research in Energetics and
Cancer and the Director of its Education, Training, and
Outreach Core. She is Co-Director of the Prevention &
Control Program at Washington University in St. Louis.
From 2003 to 2009, she was Principal Investigator and
Director of the P50 Center for Interdisciplinary Health
Disparities Research at the University of Chicago.
Bobbi J. Carothers, PhD
is a Senior Data Analyst for
the Center for Public Health Systems Science at Washing-
ton University in St. Louis. Her current responsibility is
to serve as the data analyst for several evaluations: Wash-
ington University’s Institute of Clinical and Translational
Sciences and the Washington University Transdisciplinary
Research on Energetics and Cancer. She has served as
Adjunct Faculty for the George Warren Brown School of
Social Work teaching Applied Linear Modeling.
Jung Ae Lee, PhD
is a Postdoctoral Research Associate
in the Division of Public Health Sciences, Department
of Surgery at Washington University School of Medicine
in St. Louis and also a core statistician in the Washing-
ton University Transdisciplinary Research on Energetics
and Cancer (TREC). Dr. Lee’s previous research was
built upon statistical methodology, dealing with sample
integrity problems in high dimensional low sample size
data, such as missing data, batch biases and outliers. As
a member of Washington University TREC, Dr. Lee’s
research interests lie in integrating bioinformatic tools
into translational health research, including cancer and
chronic disease control.
Jefferson Gill, PhD
is a Professor in the Departments
of Surgery, Biostatistics and Political Science, and Co-
Investigator/Core Leader for the Bioinformatics Core for
the Washington University Transdisciplinary Research
on Energetics and Cancer. He has done extensive work
in the development of Bayesian hierarchical models, non-
parametric Bayesian models, elicited prior development
from expert interviews, as well in fundamental issues in
statistical inference. He has extensive expertise in statis-
tical computing, Markov Chain Monte Carlo (MCMC)
tools in particular. Current applied work includes: en-
ergetics and cancer, long-term mental health outcomes
from children’s exposure to war, pediatric head trauma,
analysis of mouse models, and molecular models of sickle
cell disease.
Douglas Luke, PhD
is Professor and Director of the
Center for Public Health Systems Science at the George
Warren Brown School of Social Work. Dr. Luke directs
work focused primarily on the evaluation, dissemination,
and implementation of evidence-based public health poli-
cies. In addition to his appointment at the George Warren
Brown School of Social Work, Dr. Luke is a member of
the Institute for Public Health and a founding member
of the Washington University Network of Dissemination
and Implementation Researchers.
Graham Colditz, MD, PhD
is the Neiss-Gain Pro-
fessor in the School of Medicine, Chief of the Division
of Public Health Sciences in the Department of Surgery,
and the Associate Director of Prevention & Control at
the Siteman Cancer Center at Washington University in
St. Louis. He is also serving as the deputy director of
the Institute for Public Health at Washington University.
Dr. Colditz is the principal investigator for Washing-
ton University Transdisciplinary Research on Energetics
and Cancer. He is also the principal investigator of the
Siteman Cancer Center’s Program for the Elimination of
Cancer Disparities.
Transdisciplinary Journal of Engineering & Science
ISSN: 1949-0569 online, c
2014 TheATLAS
Vol. 6, pp. 1-12, (December, 2015)
