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Counselling Case Study: Relationship Problems

Mark is 28 and has been married to Sarah for six years. He works for his uncle and they regularly stay back after work to chat. Sarah has threatened to leave him if he does not spend more time with her, but when they are together, they spend most of the time arguing, so he avoids her even more. He loves her, but is finding it hard to put up with her moods. The last few weeks, he has been getting really stressed out and is having trouble sleeping. He’s made a few mistakes at work and his uncle has warned him to pick up his act.

This study deals with the first two of five sessions. The professional counsellor will be using an integrative approach, incorporating Person Centred and Behavioural Therapy techniques in the first session, moving to a Solution Focused approach in the second session. For ease of writing the Professional Counsellor is abbreviated to “C”.

After leaving school at 17, Mark completed a mechanic apprenticeship at a service station owned by his uncle and has worked there ever since. His father died from a heart attack when Mark was six years old and his uncle, who never married, has been a significant influence in his life. He is the youngest of three children, and the only boy in the family. One sister (Anne) is happily married with two children and the other (Erin) is single and works overseas. Mark and his mother have a close relationship, and he was living at home until his marriage.

Some of Mark’s friends are not married and say he was a fool for ‘getting tied down’ so young. Mark used to think that they were just jealous because Sarah is such a ‘knockout’, but lately he has started to wonder if they were right. In the last couple of months, Sarah has been less concerned about her appearance and Mark has commented on this to her. Sarah had been looking for work, but doesn’t seem to do much of anything now.

Three months ago, Sarah found out she can’t have children. According to Mark, she hadn’t spoken about wanting kids so he guessed it wasn’t a big deal to her. When she told him, Mark had joked that at least they wouldn’t have to go into debt to educate them. He thought humour was the best way to go, because he had never been very good at heavy stuff. Sarah had just looked at him and didn’t respond. He asked if she wanted to go out to a movie that night, and she had started to shout at him that he didn’t care about anyone but himself. At that point, he walked out and went to see his brother-in-law, Joe and sister, Anne.

Since then, he and Sarah hardly spoke and when they did it often turned into an argument that ended with Sarah going into the bedroom, slamming the door and crying. Mark usually walked out and drove over to Joe’s place. When Anne tried to talk to Sarah about it, Sarah got angry and told Anne to keep out of it, after all what would she know about it. She had her kids. Joe and Anne had kept their distance since then. Mark talked to his mother, but she said that this was something he and Anne had to work out together. It was she who suggested that Mark come to see C.

Session One

When Mark arrived for the first session, he seemed agitated. C spent some time developing rapport, and eventually Mark seemed to relax a bit. C described the structure of the counselling session, checked if that was ok with Mark, then asked how C could help him.

Mark: “I really wanted Sarah to come; my wife, but she said that I need to sort myself out. I have to tell you, I don’t think counselling is really for men. Women are the ones that like to talk for hours about their problems. I only came here because she insisted and I don’t want her to walk out on me.”

C: “Your marriage is important to you.”

Mark: “Yeah, sure. We’ve had fights before, but they weren’t anything major. And we always made up pretty quickly. But this is different. It seems like whatever I say is wrong, you know? Lately, I haven’t been able to concentrate properly at work and I wake up a lot through the night. I’m feeling really tired and I wish Sarah would get off my case.”

C used encouragers while Mark described what had been happening over the past few months. When he had finished ventilating his immediate concerns, C, moving into Behavioural techniques, summarized and asked Mark to decide what issue he wanted to deal with first. “Mark, you have discussed a number of issues: you are concerned that communication between you and Sarah has been reduced to mostly arguments; you’re unsure how to deal with the fact that Sarah cannot have children; you want to improve your relationship with Sarah; you are worried that Sarah might leave you, and you are feeling very stressed out. What area would you like to work on first?”

Mark: “I just want her to talk to me without arguing. All this is making it really hard for me to concentrate at work, you know.”

C: “Sounds like two goals there, to reduce your stress and to improve communication between Sarah and yourself.”

M: “Yeah, I guess so. If she would just talk to me instead of crying.”

C used open questions and reflections to encourage Mark to look at his feelings. “How do you feel when she goes into the bedroom and starts crying?” Mark: “Well, she’s never been a crier, and I don’t know what to say to her. If I mention not having children, she will probably cry even more.”

C: “So you feel confused about what to do, and anxious that you may upset her even more.”

Mark: “Yes, I just can’t seem to think straight sometimes. Like, I want things to be the way they were, but it’s just getting worse.”

C informed Mark about the use of relaxation techniques to reduce his stress and checked out if he would like to give it a try. “Mark, you appear to be having difficulty coping because you are feeling very stressed. I believe that learning relaxation techniques would decrease the level of stress and help you think more clearly. How does this sound to you?”

Mark: “I’m not into that chanting stuff if that’s what you mean.”

C explained that there are many forms of relaxation and described the deep breathing and muscle tensing method; Mark agreed to do this for 10 minutes twice a day.

As the first session drew to a close, C reviewed the relaxation technique and asked Mark to practise it as often as possible. A second appointment was arranged for the following week.

At the next session, C asked Mark how the relaxation exercise had helped. “I forget to do it some mornings, so I did it for twenty minutes at night instead. I told Sarah what I’m doing and she just leaves me to it. Not sure if it’s making any difference but I’ll keep doing it. It’s nice to have twenty minutes of peace and quiet.” At this point, C moved into a Solution Focused approach.

C congratulated Mark on commencing the relaxation practice, then checked out if it was okay to ask him some different types of questions. Mark agreed and C asked a miracle question. “Imagine that you wake up tomorrow and a miracle has happened. Your problem has been solved. What would other people notice about you that would indicate things are different?”

Mark looked at C, who waited in silence. Eventually Mark responded. “Ok, they would see me and Sarah talking a lot more, without arguing.”

C: “What else would they notice about you?”

Mark: “I would probably be spending more time at home. You know, not staying back so late at work.”

C: “What would they notice that was different about Sarah?”

Mark: “That’s easy. She wouldn’t be crying and yelling all the time.”

C: “So what would she be doing instead?”

Mark: “I guess she would be talking to me, and smiling.”

After spending some time exploring what would be different if the miracle happened, C asked Mark what he had tried in the past to improve communication. Mark revealed that he bought Sarah some flowers and a box of chocolates (his uncle’s suggestion) but it hadn’t really made any difference. C complimented Mark on his efforts and continued with an exception question.

“Can you think of a recent occasion, when you would have expected a quarrel to start and it didn’t?”

Mark furrowed his brow and appeared to be thinking deeply for some time. C waited in silence. Finally, Mark answered. “Actually, about a week ago, I was a bit late home from work and I was expecting another tongue-lashing, but it never came.”

C asked Mark what was different about that night.

Mark: “Well, Sarah was happier.”

C: “How did you know she was happier?”

Mark: “She talked to me, you know, just talked about something she had seen on the telly or something like that.”

C: “And how was that for you, Mark?”

Mark: “Not bad. Actually, it wasn’t too shabby. We did get to chat, and we haven’t done that for ages.”

C: “Can you explain, “Wasn’t too shabby”; I haven’t heard that term before?”

Mark: “Oh, it means it was all good, you know, it was okay.”

C: “So you came home and chatted with Sarah over a cuppa and you found that wasn’t too shabby?” Both smiled

Mark: “I really liked it. I remember thinking I would have come home earlier if I had known it was going to be like that.”

C: “If I was to ask Sarah what was different about that night, what do you think she would say?”

Mark: “Boy, this is getting weird.”

Mark: “Let’s see. She would probably say, “He actually sat and had a cup of coffee with me, instead of just flopping in front of the telly. She’s always griping about that.”

At the appropriate time, C called for a break. “I’d like to take a break and give us both time to consider all the things we’ve talked about. After that, I will give you some feedback.” After the break C summarized what had been discussed and complimented Mark on the work he had put into exploring his problems. He seemed less stressed and had shown that he was committed to improving his relationship with Sarah.

Counselling continued for another three sessions, by which time Mark’s stress had reduced considerably, he was coming home from work earlier and making an effort to talk more to Sarah. The arguments were less frequent and not so heated.

Session Summary

The Person Centred approach allows the client to take the lead and discuss issues as they see them. This encourages the client to talk openly, which was especially useful in this instance since the client showed a reluctance to do so at first.

The Behavioural technique of goal setting is used to clarify what the client wants to achieve out of the sessions.

Solution Focused Therapy, this approach acknowledges that the client has the ability to solve his own problem.

Miracle questions assist the client to examine how they and others would be behaving if the problem were already dealt with. This helps the client to look at their current behaviour and see what they can do to bring about the required change. Exploring what the client has tried in the past highlights that the client is committed to solving the problem. Exception questions help the client to see that there are times when the problem does not occur, and that they have contributed to that situation. This shows the client that they have control over the problem.

Clarifying client’s words, eg. “Not too shabby” shows respect for the client’s language and emphasises that the client is the expert.

Author: Jan McIntyre

Related Case Studies: A Case of Acceptance and Letting Go , A Case of Stress , A Case of Using Logical Consequences

• April 16, 2007
• Case Study , Person-centred , Solution-focused
• Case Studies , Relationship & Families

Comments: 1

Thank you! Very well explained. How to build rapport so that client loosens up.

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Rebuilding Relationships Across Teams in a Hybrid Workplace

by Ron Carucci

Summary .   

The coming year of inventing our way toward whatever our workplaces will look like offers a marvelous opportunity to refresh and reinvent cross-functional relationships. Working to rebuild these bonds is especially important because most people won’t be returning to work as the same people they were before the pandemic; the last 18 months have changed all of us in some way. Organizational fragmentation isn’t a byproduct of remote work. It results from a lack of intentional bridge-building to link discrete groups and regions. Silos were certainly prevalent before the pandemic — hybrid work has simply created new requirements for effectively connecting teams that must work together to achieve shared outcomes. The author offers three approaches to help leaders and their teams reestablish strong connections across organizational boundaries as they’ve shifted to hybrid work environments.

As people slowly return to some form of hybrid workplace, bonds that tie them to one another must be rebuilt. Over the past 18 months, most organizations have experienced some degree of fracturing as social connections and cultural cohesion have been strained. The challenges of remote work , dramatic uncertainty, the clumsy process of figuring out what returning to the office could look like, and the mass exodus of workers fed up with cultures that make them feel devalued have all served to threaten a sense of community. On top of all that, most of our remote work interactions have been with our immediate colleagues and focused largely on the tasks at hand — research from Microsoft suggests that cross-functional collaboration went down by 25% as interactions within groups increased during the pandemic.

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• Published: 06 May 2021

Interpersonal relationships drive successful team science: an exemplary case-based study

• Hannah B. Love   ORCID: orcid.org/0000-0003-0011-1328 1 ,
• Jennifer E. Cross   ORCID: orcid.org/0000-0002-5582-4192 2 ,
• Bailey Fosdick   ORCID: orcid.org/0000-0003-3736-2219 2 ,
• Kevin R. Crooks 2 ,
• Susan VandeWoude 2 &
• Ellen R. Fisher 3  

Humanities and Social Sciences Communications volume  8 , Article number:  106 ( 2021 ) Cite this article

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• Complex networks
• Science, technology and society

Scientists are increasingly charged with solving complex societal, health, and environmental problems. These systemic problems require teams of expert scientists to tackle research questions through collaboration, coordination, creation of shared terminology, and complex social and intellectual processes. Despite the essential need for such interdisciplinary interactions, little research has examined the impact of scientific team support measures like training, facilitation, team building, and expertise. The literature is clear that solving complex problems requires more than contributory expertise, expertise required to contribute to a field or discipline. It also requires interactional expertise, socialised knowledge that includes socialisation into the practices of an expert group. These forms of expertise are often tacit and therefore difficult to access, and studies about how they are intertwined are nearly non-existent. Most of the published work in this area utilises archival data analysis, not individual team behaviour and assessment. This study addresses the call of numerous studies to use mixed-methods and social network analysis to investigate scientific team formation and success. This longitudinal case-based study evaluates the following question: How are scientific productivity, advice, and mentoring networks intertwined on a successful interdisciplinary scientific team? This study used applied social network surveys, participant observation, focus groups, interviews, and historical social network data to assess this specific team and assessed processes and practices to train new scientists over a 15-year period. Four major implications arose from our analysis: (1) interactional expertise and contributory expertise are intertwined in the process of scientific discovery; (2) team size and interdisciplinary knowledge effectively and efficiently train early career scientists; (3) integration of teaching/training, research/discovery, and extension/engagement enhances outcomes; and, (4) interdisciplinary scientific progress benefits significantly when interpersonal relationships among scientists from diverse disciplines are formed. This case-based study increases understanding of the development and processes of an exemplary team and provides valuable insights about interactions that enhance scientific expertise to train interdisciplinary scientists.

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Introduction.

Scientists are increasingly charged with solving complex and large-scale societal, health, and environmental challenges (Read et al., 2016 ; Stokols et al., 2008 ). These systemic problems require interdisciplinary teams to tackle research questions through collaboration, coordination, creation of shared terminology, and complex social and intellectual processes (Barge and Shockley-Zalabak, 2008 ; De Montjoye et al., 2014 ; Fiore, 2008 ). Thus, to successfully approach complex research questions, scientific teams must synthesise knowledge from different disciplines, create a shared terminology, and engage members of a diverse research community (Matthews et al., 2019 ; Read et al., 2016 ). Despite significant time, energy, and money spent on collaboration and interdisciplinary projects, little research has examined the impact of scientific team support measures like training, facilitation, team building, and team performance metrics (Falk-Krzesinski et al., 2011 ; Klein et al., 2009 ).

Studies examining the development of scientific teaming skills that result in successful outcomes are sparse (Fiore, 2008 ; Hall et al., 2018 ; Wooten et al., 2014 ). The earliest studies of collaboration in science used bibliometric data to search for predictors of team success such as team diversity, size, geographical proximity, inter-university collaboration, and repeat collaborations (Borner et al., 2010 ; Cummings and Kiesler, 2008 ; Wuchty et al., 2007 ). Building from these studies, current research focuses on team processes. Literature suggests that to successfully frame a scientific problem, a team must also engage emotionally and interact effectively (Boix Mansilla et al., 2016 ) and that scientific collaboration involve consideration of the process, collaborator, human capital, and other factors that define an scientific collaboration (Bozeman et al., 2013 ; Hall et al., 2019 ; Lee and Bozeman, 2005 ). Similarly, Zhang et al. ( 2020 ) used social network analysis to examine how emotional intelligence is transmitted to team outcomes through team processes. Still more research is needed, and Hall et al. ( 2018 ) called for team science studies that use longitudinal designs and mixed-methods to examine project teams as they develop in order to move beyond bibliometric measures of success and to explore the complex, interacting features in real-world teams.

Fiore ( 2008 ) explained that much of what we know about the science of team science (SciTS), training scientists and team learning in productive team interactions, is anecdotal and not the result of systematic investigation (Fiore, 2008 ). Over a decade later there is still a paucity of research on how scientific teams develop the type of expertise they need to create new knowledge and further scientific discovery (Bammer et al., 2020 ). Bammer et al. ( 2020 ) has identified and defined two types of expertise: (1) contributory expertise, expertise required to make a contribution to a field or discipline (Collins and Evans, 2007 ); and (2) interactional expertise, socialised knowledge that includes socialisation into the practices of an expert group (Bammer et al., 2020 ). These forms of expertise are often tacit, codified by “learning-by-doing,” and augmented from project to project; therefore, they are difficult to measure and rarely documented in literature (Bammer et al., 2020 ).

Wooten et al. ( 2014 ) outlined three types of evaluations—developmental, process, and outcome—needed to understand how teams develop and to provide information about their future success (Wooten et al., 2014 ). A developmental evaluation focuses on the continuous process of team development, and a process evaluation focuses on team interactions, meetings, and engagement (Patton, 2011 ). Both development and process evaluations have the common goal of understanding the team’s future success or failures, also known as the team’s outcomes (e.g., grants, publications, and awards) (Patton, 2011 ). The majority of published work on outcome metrics is evaluated by archival data analysis, not individual team behaviour and assessment (Hall et al., 2018 ). Albeit informative, these studies are based upon limited outcome metrics such as publications and represent only a selective sampling of teams that have achieved success. To collect these three types of evaluation data, it is recommended to engage mixed-methods research such as a combination of social network analysis (SNA), participant observation, surveys, and interviews, although these approaches have not been widely employed (Bennett, 2011 ; Borner et al., 2010 ; Fiore, 2008 ; Hall et al., 2018 ; Wooten et al., 2014 ).

A few key studies have provided insight into successful collaboration strategies. Duhigg ( 2016 ) found that successful teams provided psychological safety, had dependable team members, and relied upon clear roles and structures. In addition, successful teams had meaningful goals, and team members felt like they could make an impact through their work on the team (Duhigg, 2016 ). Similarly, Collins ( 2001 ) explained that in business teams, moving from “Good to Great” required more than selecting the right people; the team needed development and training to achieve their goals (Collins, 2001 ). Woolley et al. ( 2010 ) found that it is not collective intelligence that builds the most effective teams, but rather, how teams interact that predicts their success (Woolley et al., 2010 ). The three traits they identified as most associated with team success included even turn-taking, social sensitivity, and proportion female (when women’s representation nears parity with men) (Woolley et al., 2010 ). Finally, Bammer et al. ( 2020 ) recommended creating a knowledge bank to strengthen knowledge about contributary and interactional expertise in scientific literature to solve complex problems. Collectively, these studies argue that the key to collective intelligence is highly reliant on interpersonal relationships to drive team success.

This article reports on a longitudinal case-based study of an exemplary interdisciplinary scientific team that has been successful in typical scientific outputs, including competing for research awards, publishing academic articles, and training and developing scientists. This analysis examines how scientific productivity, advice, and mentoring networks intertwined to promote team success. The study highlights how the team’s processes to train scientists (e.g., developing mentoring and advice networks) have propelled their scientific productivity, fulfilled the University’s land grant mission (i.e., emphasises research/discovery; education/training; and outreach/engagement) and created contributory and interactional expertise on the team. Team dynamics were evaluated by social network surveys, participant observation, focus groups, interviews, and historical social network data over 15 years to develop theory and evaluate complex relationships contributing to team success (Dozier et al., 2014 ; Greenwood, 1993 ).

Case study selection

The [BLIND] Science of team science (SciTS) team consisted of scientists trained in four different disciplines and research administrators. The SciTS team monitored twenty-five interdisciplinary teams at [BLIND] for 5 years from initiation of team formation to identify team dynamics that related to team success. This case is thus presented as part of an ongoing study of the 25 teams, supported by efforts through the [BLINDED] to encourage and enhance collaborative, interdisciplinary research and scholarship. Team outcomes were recorded annually and included extramural awards, publications, presentations, students trained, and training outcomes. An exemplary case-based study is appropriate when the case is unusual, the issues are theoretically important, and there are practical implications (Yin, 2017 ). Further, cases can illustrate examples of expertise and provide guidance to future teams (Bammer et al., 2020 ). An “exemplary team designation” was given to this team by the SciTS evaluators. Metrics used to designate an exemplary team included: team outcomes; highly interdisciplinary research; longevity of the team; fulfilment of all aspects of the land grant mission (research/discovery; education/training; and outreach/engagement); integration of team members; and use of external reviewers.

Social network survey

The exemplary team included Principle Investigators (PIs), postdoctoral researchers (postdocs), graduate students, undergraduate students, and active collaborators external to the University. The entire team was surveyed annually 2015–2019 about the extent and type of collaboration with other team members. In 2015, the team was asked about prior collaborations, and in subsequent years they were asked about additional interactions since joining the team. Possible collaborative activities included research publications, scientific presentations, grant proposals, and serving on student committees. Team members were also asked the types of relationships they had with each team member, including learning, leadership, mentoring, advice, friendship, and having fun (Supplementary 2 ). Data were collected using a voluntary online survey tool (Organisational Network Analysis Surveys). All subjects were identified by name on the social network survey but are not identified in any network diagrams or analyses. SNA software programmes R Studio (R Studio Team, 2020 ) and UCINET (Borgatti et al., 2014 ) were used to analyse data and Visone (Brandes and Wagner, 2011 ) was used to create visualisations. The response rate for the survey was 94% in 2015, 83% in 2016, 95% in 2017, and 81% in 2018. All data collection methods were performed with the informed consent of the participants and followed Institutional Review Board protocol #19-8622H.

Data from the social network survey were combined to create three different network measures: scientific productivity, mentoring, and advice. The scientific productivity network was a combination of four survey measures: research/consulting, grants, publications, and serving on student committees. Scientific productivity represents a form of cognitive or contributory expertise: expertise required to contribute to a field or discipline (Bammer et al., 2020 ; Boix Mansilla et al., 2016 ). The mentoring and advice networks were created from social network survey questions: “who is your mentor?” and “who do you go to for advice?”, respectively. Mentor and advice are tacit forms of interactional expertise: socialised knowledge that includes socialisation into the practices of an expert group (Collins and Evans, 2007 ). Other studies have also found a connection between social characteristics of interdisciplinary work and other factors like productivity, career paths, and a group’s ability to exchange information, interact, and explore together (Boix Mansilla et al., 2016 ).

Social network data were summarised using average degree, sometimes split into indegree and outdegree. Outdegree is a measure of how many team members a given individual reported getting advice, or mentorship, from. Similarly, the indegree of an individual is a measure of how many other team members reported receiving advice, or mentorship, from that person. Average degree is the average number of immediate connections (i.e., indegree plus outdegree) for a person in a network (Giuffre, 2013 ; R. Hanneman and Riddle, 2005 a, 2005 b). To further explore the mentoring and advice networks, we calculated the average degree/outdegree/indegree of postdocs, graduate students, and faculty separately to directly compare demographic groups.

The advice, mentoring, and scientific productivity networks were directly compared using the Pearson correlation between the corresponding network adjacency matrices. We predicted a positive correlation between the advice, mentoring, and scientific productivity matrices. Statistical significance ( p  < 0.05) of correlations was assessed with the network permutation-based method Quadratic Assignment Procedure (QAP) (R. A. Hanneman and Riddle, 2005 a, 2005 b).

Historical social network data

A historical network survey was created to determine how the connections in the network formed, developed, and changed from project-to-project. The historical social network was constructed from three forms of data: interviews with the PIs, a historical narrative written by the PIs describing the team formation process, and team rosters that listed the 81 team members since the inception of the team.

Retrospective team survey

A retrospective team survey was administered at the end of the study to determine what skills team members developed and codified through participating on the team, how membership on the team supported members personally and professionally, and their favourite aspects of the team. The survey was sent to 22 members from the 2018 team roster using Qualtrics (Qualtrics Labs, 2005 ) with an 86% response rate.

Two semi-structured, one-hour interviews were conducted with two PIs in 2018 to learn about the history of the team. The interviews were digitally recorded and transcribed.

Participant observations

Participant observation was conducted from 2015–2019 at four annual three-day, off-campus retreats and 1–2 additional meetings each year. Students, PIs, external collaborators, and families were all invited to attend the retreats and meetings. Field notes about team interactions were recorded immediately after each interaction. The analytic field notes captured how team members interacted across disciplines, tackled scientific problems, and engaged with others at different career stages. Analysis occurred as field notes were written, during observations, and again during data analysis.

An exemplary team

The SciTS Team identified one team from the larger study and designated it as exemplary based on six (tacit and non-tacit) elements. First, the team had outstanding team outcomes. From 2004–2018, notable accomplishments include 33 extramural awards totalling over $5.6 million, including two large federal awards totalling over $4.5 million; 58 peer-reviewed publications with 39 different universities, 13 state agencies, and 11 other organisations; 141 presentations, 21 graduate students and 15 postdocs trained; and receipt of an [BLIND]institution-wide Interdisciplinary Scholarship Team Award. Participants received many individual honours, including one of the PIs being named to the National Academy of Sciences.

Second, this interdisciplinary team combined scientific expertise from many different backgrounds, including ecologists, wildlife biologists, evolutionary biologists, geneticists, veterinarians, and numerous collaborators. Principal Investigators were housed in five main universities: Colorado State University, University of Wyoming, University of Minnesota, University of California-Davis, and University of Tasmania. They also engaged collaborators from national and international universities, federal, state, and local governmental agencies, veterinary centres, and animal shelters. Collectively, team members represented 39 different universities, 11 federal agencies, 13 state agencies, and 11 other organisations listed on their peer-reviewed publications. The team has published globally with co-authors from every continent but Antarctica.

The third element identified was the team’s 15-year history and how they evolved project-to-project (Supplementary Video S1 ). In 2003, a graduate student proposed a collaborative research project between two faculty members who became two of the founding team PIs (Fig. 1 ). The team was formed in 2004 with four members—two faculty PIs, a postdoc, and a Ph.D. student (Fig. 1 ). Initial grant proposals submitted in 2005 and 2006 were not funded; however, in 2007, the team received a large federal research award from the US National Science Foundation (NSF). The team roster increased from four to nine, and a second large expansion occurred after receipt of another NSF award in 2012. By 2014, membership increased to 31 people, and at the end of analysis in 2018, the roster comprised 43 members. Over the course of observation, 81 different individuals, including students, faculty, and collaborators, had participated in research activities supported by the team.

Significant events occurring over 15 years during the development and formation of an exemplary team.

The fourth reason this team was deemed exemplary was because it intertwined the components in the Land Grand mission, including research/discovery, teaching/training, and extension/engagement (Fig. 2 ). The team included undergraduates conducting research and presenting at conferences, graduate students working in multiple labs, and postdocs mentoring all the researchers in the lab. An external advisor said at the end of a retreat, “It’s really cool that students are part of the conversations that are both good/bad/ugly etc. It is not just good. It is not just one-on-one conversations. They hear it all.” A Ph.D. student wrote in the Retrospective Survey about the skills he developed: “I have developed the ability to talk about my research to people outside my field. I have also worked on broadening my understanding of disease ecology as a whole. I have been given the opportunity [to] begin placing my work in the larger framework of ecosystem health.” Faculty also wrote about what they learned, “[I] Learned from leadership of team (especially [blinded], and other PIs) how to develop and conduct research team work well - am using what I am learning to develop new research teams…. how to develop and nurture and respect interpersonal relationships and diversity of opinions. This has been an amazing experience, to be part of a well-functioning team, and to examine why and how that is maintained”

The team grew from 4 members in 2004 to 42 members in 2018. Much of the growth occurred by the addition of students and external collaborators.

Fifth, the team was effective at onboarding and integrating new members. To do so, they used two key strategies (Fig. 3 ). First, 15 of the students held co-advised graduate research positions. This shared model of mentorship provided students with opportunities to work in multiple labs, collaborate with additional team members, and gain a broader academic experience. A Ph.D. student wrote in the Retrospective Survey about the skills she learned from being a member of the team: “Leadership skills, communicating science to those in other fields, scientific writing skills, technical laboratory skills, interpersonal communication skills, data sharing experience, and many others.” The shared model supported the team’s interdisciplinary mission by providing opportunities to train future scientists to communicate, network, and conduct research across disciplines. Second, as team members developed through participation on the team, they assumed more mature scientific roles. Fourteen members of the team changed positions within the team. Many of these transitions were from undergraduate student to Ph.D. student or Ph.D. student to postdoctoral researcher. In 2012, one postdoc became a PI on the grant.

Social network diagrams of team growth and development from 2004–2018. This network reports onboarding and integration of all members, including their primary position when they joined the team. The nodes are sized by average degree (see text). Colours denote different roles on the team.

Finally, the 2018 team retreat included external reviewers. At the end of 2018 team retreat, they were asked if they had any feedback for the team. An external reviewer said: “You can check all of the boxes of a good team.”; “This is a dream team.”; “I am really impressed.”. Another external reviewer said:

The ambitiousness to execute the scope of the project, to have this many PIs, to be able to communicate; the opportunities for new insights; and the opportunities it presents for trainees are rare. There are a lot of people exposed in this. This is a unique experience for someone in training. And it extends to elementary school. I don’t think there are many projects that have this type of scope. I was impressed with just the idea that scientists are taking this across such a great scope and taking on such great questions.

Scientific productivity network

Prior to 2016, the average degree of the scientific productivity network was 8.8 (Fig. 4 ). In 2016, four faculty nodes were in the core of the network, and the periphery nodes included graduate students, postdocs, and external collaborators (Fig. 5 ). The average degree dropped slightly to 6.2 when the team integrated new members and re-formed around new roles and responsibilities on a new grant (Fig. 4 ). In 2017, the average degree peaked at 9.7 (Fig. 4 ) and faculty were still core, but graduate students and postdocs were more central than before (Fig. 5 ). During this time, productivity was at its highest as team members were working together to meet the objectives of a 5-year interdisciplinary NSF award. The network evolved further in 2018; two of the postdoc nodes overlapped with the faculty nodes in the core of the network (Fig. 5 ).

Average degree of social networks diagrams (mentoring, advice, scientific productivity) indicated strong social ties among team members.

Social network measures of productivity (research/consulting, grants, publications, and serving on student committees) were recorded over time. Each node represents a person on the team, and nodes are sized by average degree (see text). Colours denote different roles on the team. The node label indicates the number of years a person has been part of the team.

Mentoring is integral in the collaborative network

Team members reported between an average of 2.4–3.1 mentors (average outdegree) each year on the team (Fig. 6 ). More specifically, graduate students reported 6.0–7.7 mentors, whereas postdocs reported 2.4–3.5 mentors (Table 1 ). Faculty team members reported having an average of 2.2 to 4.3 mentors on the team (Table 1 ), with the highest average outdegree in 2018.

This diagram was created by using participant answers to the social network question, “who is your mentor?” Each circle or node represents a person on the team. The nodes are sized by outdegree to show who reported receiving mentorship. Node size indicates how many mentors an individual reported, and arrows indicate nodes that served as mentors. Colours denote different roles on the team.

The highest indegree for an individual was the lead PI, with an indegree ranging from 13 to 14 each year (i.e., each year, 13–14 team members reported this individual provided mentorship). In response to an interview question about this PIs favourite part of the team, this individual said, “…and of course, I really like the mentorship of the students…They are initially naive, and some people are initially underconfident, but eventually they become fluent in their subject area.” Many students wrote about the mentoring they received from the team. An undergraduate student wrote:

I have improved my communication skills after needing to collaborate with several mentors across different time zones. I’ve also improved willingness to ask questions when I don’t understand a concept. I’ve also learned what concepts I find basic in my field that others outside my discipline are less familiar with.

Faculty also wrote about the mentoring they received, such as, “I continually learn from members in the team and mentorship by the more experienced members has supported my own career progression.”

Advice is integral in the collaborative network

In the 2015–2017 advice network diagrams, the faculty were tightly clustered (Fig. 7 ). In 2018, the cluster separated as postdocs and graduate students joined the centre of the network. On average, team members reported 5.1 to 6.4 people they could go to for advice (Fig. 4 ).

This diagram was created by using participant answers to the social network question, “who do you go to for advice?” Each circle or node represents a person on the team. The nodes are sized by outdegree to show who reported receiving mentorship. Node size indicates how many mentors an individual reported, and arrows indicate nodes that served as mentors. Colours denote different roles on the team.

In a survey, faculty responded to the question, “How has the team supported you personally and professionally?” One faculty member wrote: “Just today I asked three members of the team for professional advice! And got a thoughtful and prompt response from all.” Another team member wrote: “Being a member of the…team has allowed me to develop skills in statistical analysis, scientific writing, and critical thinking. This team has opened my eyes to what is possible to achieve with science and has provided me with opportunities to network and expand my horizons both within the field of study and outside of it.” These quotes further suggest that the mentoring and advice from a large interdisciplinary team were important to train future scientists.

Interpersonal relationships as driver for scientific productivity

The mentoring and advice networks supported and built on the scientific productivity network and vice versa. The correlation between the collaboration, mentoring, and advice networks would not be possible if the networks were not intertwined. In the retrospective survey, a faculty member described how tacit interpersonal relationships were correlated with their scientific productivity:

Being a part of this grant has helped me both personally and professionally by teaching me new skills (disease ecology, team dynamics), developing friendships/mentors from the team, and strengthening my CV and dossier for promotion to early full professorship.

A Ph.D. student also described how the relationships on the large team propelled their research.

Membership on this team has provided me with a lot of mentorship that I would not otherwise receive were I not working on a large multi-disciplinary for my doctoral research. It has also allowed me to network more effectively.

Between 2015 and 2018, the mentor and advice networks were significantly correlated with the scientific productivity network, demonstrating that personal relationships are associated with scientific collaboration (Table 2 ).

To date, the literature examining successful interdisciplinary scientific team skills that result in successful outcomes is sparse (Fiore, 2008 ; Hall et al., 2018 ; Wooten et al., 2014 ). The majority of published work in this area is evaluated by archival data analysis, not individual team behaviour and assessment (Hall et al., 2018 ). This study answers the call of numerous researchers to use mixed-methods and SNA to investigate scientific teams (Bennett, 2011 ; Borner et al., 2010 ; Hall et al., 2018 ; Woolley et al., 2010 ; Wooten et al., 2015 ). Our case-based study also increases understanding of the development and processes of an exemplary team by providing valuable insights about how the interactions that enhance scientific productivity are synergistic with the interactions that train future scientists. There are four major implications of our findings: (1) interactional and contributory expertise are intertwined; (2) team size, tacit knowledge gained from previous project, and interdisciplinary knowledge were used to effectively and efficiently train scientists; (3) the team increased scientific productivity through interpersonal relationships; and (4) the team fulfilled the land grant mission of the University by integrating teaching/training, research/discovery, and extension/engagement into the team’s activities.

Interactive and contributory expertise are intertwined

Previous literature on scientific teams has found that great teams are not built on scientific expertise alone, but on the processes and interactions that build psychological safety, create a shared language, engage members emotionally, and promote effective interactions (Boix Mansilla et al., 2016 ; Hall et al., 2019 ; Senge, 1991 ; Woolley et al., 2010 ; Zhang et al., 2020 ). The team highlighted in this report created a shared language and vision through the mentoring and advice networks that helped fuel the team’s scientific productivity (Hall et al., 2012 ). To solve complex problems requires more than contributory expertise, it also requires interactional expertise (Bammer et al., 2020 ). These forms of expertise are often tacit and internalised through the process of becoming an expert in a field of study (Collins and Evans, 2007 ). Learning-by-doing is augmented from project-to-project, with expertise codified over time (Bammer et al., 2020 ). Further, cognitive, emotional, and interactions are key components of successful collaborations (Boix Mansilla et al., 2016 ; Bozeman et al., 2013 ; Zhang et al., 2020 ). Using social network analysis, our case-based analysis found that the mentoring and advice ties were intertwined with the scientific productivity network.

Training scientists to be experts

The Retrospective Survey asked what personal and professional skills respondents learned from being a member of a team. We hypothesised that many respondents would report tangible skills. Surprisingly, 82% of the open-ended responses were about tacit skills. Students frequently had co-advised graduate research positions, worked in multiple labs, and communicated regularly with practitioners. Moreover, the team translated research to different disciplines within the team, mentored others, and managed interpersonal conflicts. These interactions built expertise because training was not limited to research in a single lab or only in an academic setting. Simple, discrete, and codified knowledge is relatively easy to transfer; however, teams need stronger relationships to gain complex and tacit knowledge, (Attewell, 1992 ; Simonin, 1999 ). On this team, interactions and the ability to practice communication were especially influential for students, junior scientists, and new members. These individuals provided survey responses reporting they learned a wide variety of skills ranging from leadership, scientific and interpersonal communication, networking across disciplines, scientific writing, laboratory techniques, and data sharing standards. Further, respondents noted they had gained experience in developing, nurturing, and respecting interpersonal relationships and diversity of opinions. This was reinforced with participant observation data. In other interdisciplinary groups studied in conjunction with this exemplary team, students were not typically exposed to the inner workings of the team such as leadership meetings. On this team, students were exposed to all conversations, which became an important component of the mentoring and advice structure, serving to train future scientists in all aspects of team integration and leadership development. Belonging to this large interdisciplinary team was effectively training, building, and structuring the team.

Interpersonal relationships increase scientific productivity

Longevity of relationships is an important factor in creating social cohesion, reducing uncertainty, and increasing reliability and reciprocity (Baum et al., 2007 ; Gulati and Gargiulo, 1999 ; Phelps et al., 2012 ). Previous literature has, however, rarely documented the importance of time in building the structure of the network (Phelps et al., 2012 ) and few longitudinal studies of scientific teams exist. Further, it has long been hypothesised that greater interaction among people increases the quality and innovativeness of ideas generated, which may in turn increase productivity (Cimenler et al., 2016 ). Our case-based study found that the mentoring and advice ties existed in a symbiotic relationship with the scientific productivity network where the practices of the team were simultaneously training scientists. This aligns with social network literature that interactions can structure the social network and the network structure influences interactions (Henry, 2009 ; Phelps et al., 2012 ). Second, intentional mentoring programmes have demonstrated a positive relationship between interdisciplinary mentoring and increased research productivity outcomes such as grant funding and publications (Spence et al., 2018 ). Finally, this finding also aligns with literature on the generation of new knowledge (Phelps et al., 2012 ). Knowledge creation has traditionally been framed in terms of individual creativity, but recent studies have placed more emphasis on how the contribution of social dynamics are influential in explaining this process (Boix Mansilla et al., 2016 ; Csikszentmihalyi, 1998 ; Phelps et al., 2012 ; Sawyer, 2003 ; Zhang et al., 2009 ). Thus, while we might think that science drives the team, in this case-based study, the team’s interpersonal relationships were the driver of the team’s scientific productivity.

Fulfilling the land grant mission

As noted above, this exemplary team fulfilled all three goals of the land grant mission. First, the team was training scientists at all levels, from undergraduate students, to graduate students, postdocs, new faculty, and external collaborators, including community partners. In many instances, the training and mentoring was structured in a vertically integrated manner. For example, postdocs were training graduate and undergraduate students, typical of many teams. In addition to the “top-down” scenarios, however, the team also encouraged training that went from the bottom up as well. Effectively, this is a hallmark of successful teams in other sectors such as emergency responders and elite military teams – whomever has the knowledge to drive the issue at hand is the effective “leader” in that mission (Kotler and Wheal, 2008 ). Second, the team excelled in research and discovery, partnering with a diversity of external collaborators to do so. This created a network structure wherein the team clearly utilised the collaborators for mentoring and advice. Organisations with a core-periphery network structure like this team have been reported to be more creative because ties on the periphery, such as external collaborators, can span boundaries and access diverse information (Perry-Smith, 2006 ; Phelps et al., 2012 ). Finally, because the team’s collaborators included community partners and practitioners, they were also influencing policy and practice. This resulted in an overall greater impact for the team’s science and allowed them to tailor their research to best meet the needs of society (Barge and Shockley-Zalabak, 2008 ).

Future research

This study provides a unique contribution to team science literature because it longitudinally studied the development and processes of a successful interdisciplinary team (Wooten et al., 2014 ). Future research on the elements of effective interdisciplinary teaming is required in five key areas. First, identification of best practices that inhibit or support teams is necessary (Fiore, 2008 ; Hall et al., 2018 ; Wooten et al., 2014 ). Second, previous research has found that small teams are best at disrupting science with new ideas and opportunities (Wu et al., 2019 ); however, practices large teams use to create new knowledge have been poorly documented. Third, successful training concepts for graduate students and postdoctoral researchers need additional consideration (Knowlton et al., 2014 ; Ryan et al., 2012 ; Sarraj et al., 2017 ). Fourth, we hypothesise that graduate students act as bridges in teams to connect scientific disciplines and prevent clustering the network. Future research should investigate the role of graduate students in creating knowledge through interdisciplinary teams. Finally, additional research is needed to better recognise and reward scientists who undertake integration and implementation (Bammer et al., 2020 ).

Data availability

The datasets generated during and analysed during the current study are available in the Mountain Scholar repository, https://doi.org/10.25675/10217/214187

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Acknowledgements

A special thank you to Elizabeth Scodfidio for helping with data, images and more!. The research reported in this publication was supported by Colorado State University’s Office of the Vice President for Research Catalyst for Innovative Partnerships Programme. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Office of the Vice President for Research. Supported by NIH/NCATS Colorado CTSA Grant Number UL1 TR002535. Contents are the authors’ sole responsibility and do not necessarily represent official NIH views. Funding and support were provided by grants from the National Science Foundation’s Ecology of Infectious Diseases Programme (NSF EF-0723676 and NSF EF-1413925).

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HBL conceptualised the study, developed the methodology, curated the data, analysed the data, conducted the investigation, worked as the project manager, managed the software, validated the data, created visualisations, reviewed and edited the paper; BF conceptualised the study, developed the methodology, curated the data, analysed the data, managed the software, validated the data, supervised all aspects of the research, created visualisations, reviewed and edited the paper; JC conceptualised the study, developed the methodology, acquired funding, supervised data collection, and reviewed and edited the paper; KC and SV wrote the paper, secured funding, reviewed and edited the paper; and ERF conceptualised the study, developed the methodology, supervised all aspects of the research, acquired funding, created the visualisations, reviewed and edited the paper; All authors reviewed the manuscript.

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Love, H.B., Cross, J.E., Fosdick, B. et al. Interpersonal relationships drive successful team science: an exemplary case-based study. Humanit Soc Sci Commun 8 , 106 (2021). https://doi.org/10.1057/s41599-021-00789-8

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You know the drill …

Enter pain. Enter fear. Enter shame. Enter defensiveness. And, of course, enter those inevitable, manic swings between the land of “how dare they not recognize someone of my literary caliber by immediately publishing my work and renaming their site in my honor” and “of course they didn’t publish it … I can barely string together words, much less justify my existence as a person.”

Like a friend of mine likes to say, “My brain’s constantly telling me I’m either hot s*** or a piece of s***.”

But enough about my existential angst. Pain, fear, shame, defensiveness, and manic swings aside, I eventually picked myself up, dusted off, and reworked the thing for another blog.

More to the point, back some thirty-plus days ago, I promised a more lengthy explanation of how those massive wins — namely writing for Fast Company, Copyblogger, MarketingProfs, Entrepreneur, and now Unbounce — came together.

So today I present the first of three case studies on growth and success:

Build relationships..

Cliches are cliche for a reason. Because they’re true.

“It’s not about what you know. It’s about who you know.”

At the beginning of last summer, when I finally decided to make a go of this whole “freelance writing” thing, I identified a handful of people who — number one — I liked and respected and — number two — mattered.

Demian Farnworth, Copyblogger’s chief copywriter, was at the very top of that list.

I’ve loved Copyblogger (who doesn’t?) for years and dreamed of writing for them.

A few months ago, I noticed Demian’s name kept popping up and always in connection with absolutely stellar content. The first one I really loved was “13 Good Ideas from 13 Dead Copywriters.” And, of course, his “11 Essential Ingredients Every Blog Post Needs” is on just about everybody’s list of the best content of 2014.

So I tracked Demian down, digitally speaking, and found out not only did he run a personal copywriting site — theCopyBot — he also ran an even more personal “personal” site — Fallen + Flawed .

What I found on the second floored me.

Fallen + Flawed is not a marketing site. It isn’t about improving your CTRs, optimizing your headlines, telling your story, or getting your emails opened. In fact, it’s not really about writing at all — although there are posts about blogging .

Fallen + Flawed is about the gospel . It’s about faith and life and struggling and books and marriage and kids and Mexican wrestlers .

It is creative. It is original. It is authentic. And it is phenomenal.

On top of all that, the theological overlaps were deep.

Even within the (for lack of a better word) “evangelical” Christian community (with which I self-identify), there are camps, especially theological camps. And every camp has its patron saints. For the young, reformed community (the camp I belong to), folks like John Piper, D. A. Carson, and Tim Keller are rockstars.

And that’s exactly who Demian was writing about.

I nearly lost my mind.

I was so excited that I started tweeting anything I could get ahold of.

I tweeted his Copyblogger stuff. I tweeted his CopyBot stuff. I tweeted his theological stuff. I even tweeted lines from his fantastic ebook The Messiah | Eleven Meditations from the Book of Mark .

And … I tagged him each and every time.

By now you’ve probably picked up that I’m a little biased, but let me just say that Demian is a super decent dude. I don’t think there’s been a single tweet I tagged him in that he didn’t favorite. Plus, right around the time I started tweeting stuff from his book on Mark, he tweeted back.

Mind = blown.

We threw a few notes around, mostly just polite niceties, and then … I found his coaching page.

This time, I emailed. And — gasp — he emailed back.

I won’t go into detail here about what an amazing coach Demian has been (after all, I’ve probably gushed enough).

Here’s the point …

When it finally came time to submit a guest post to Copyblogger, I did so both through their online submission process as well as a direct email to Demian himself.

From the first — their online process — all I got back was a “Thanks for submitting … but we aren’t accepting unsolicited articles at this time.”

It was the second route — the relationship route — that opened the door.

Now, of course, you gotta have the goods as well as the relationship. I mean, no one’s gonna publish junk. In fact, I’m gonna say a lot more about having “the goods” in the second case study — “Write [bleeping] amazing content” — but for now it’s vitally important to see how central that relationship was.

What made the difference was not first and foremost the words themselves.

What made the difference was that relationship — that authentic, genuine, giving (not just taking) relationship.

A similar situation has developed with Joanna Wiebe of CopyHackers . She and her team — including her awesome copywriting compadre Jen Havice — have been incredibly supportive of both my personal blog and guest posts. I remember the day she signed up for my email list … again: mind = blown.

The same is true of Ann Handley , who after quoting a post of mine in her most recent book, has also been amazing.

So, let me ask you …

Who do you look up to? Who do you admire? Who would you want to connect with? Who do you respect? Who matters in your industry?

Alright … what’s stopping you from reaching out?

Track ‘em down. Share their content. Tag ‘em. Be warm and be personal. Write meaningful comments on their blog. Ask insightful questions. And above all, be real.

Over the next month, I’ll be sharing three more case studies on growth and success. Here’s a preview …

2. Write [bleeping] amazing content.

3. Be a decent freakin’ person.

4. Go after it … again, and again, and again.

Let’s save the world from bad content

Download the …

Comprehensive Content Strategy Roadmap

This is the exact resource I’ve used with clients starting at $10k per month . But, you can have it for free. (Just don’t tell anybody.)