Mapping Out Action Plan

ORIGINAL ARTICLE

Interprofessional collaboration in mental health settings: a social network analysis Chiara Pomare , Janet C Long , Louise A Ellis , Kate Churruca , and Jeffrey Braithwaite

Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia

ABSTRACT This paper provides the first assessment of patterns of interprofessional collaboration in headspace centres – Australia’s youth mental health service – to determine if agencies established to integrate care, deliver collaboration across professional boundaries. The staff of two headspace centres were surveyed to identify with whom they collaborated during routine work, and when faced with uncertain situations. Social network analysis was used to assess interprofessional collaboration within each center and across varying situations. Interprofessional collaboration was identified in both routine and uncertain situa- tions. Staff of headspace (clinical and non-clinical) maintained a tendency to collaborate with colleagues outside of their professional group, compared to within. Networks were well connected when staff collaborated in routine work and when faced with uncertainty related to decision-making. However, there were fewer interactions during times of role uncertainty. The headspace centre that had been in operation for longer showed greater indicators of cohesiveness. Future research should consider context and self-organization when considering the efficacy of networks.

ARTICLE HISTORY Received 7 February 2018 Revised 12 June 2018 Accepted 30 October 2018

KEYWORDS Interprofessional collaboration; mental health care; social network analysis; uncertainty

Introduction

Interprofessional collaboration refers to the integration of diverse professionals working interdependently towards a common goal (Reeves, Lewin, Espin, & Zwarenstein, 2010). Such patterns of interprofessional working are fostered in models of integrated care, that strive to align fragmented services within the health-care system (Porter & Lee, 2013). There has been an international trend towards integrated youth health care, advocating for the collaboration and co- location of diverse clinical and non-clinical staff to improve the quality and safety of youth health care (Hetrick et al., 2017). This trend is supported by a recent systematic review that showed greater clinical improvements for young people with mental health conditions who have accessed integrated youth health care, compared to those who received usual care (i.e., care provided serially by individual professionals) (Asarnow, Rozenman, Wiblin, & Zeltzer, 2015). Good inte- gration of care is particularly important in mental health care (MHC), which has been conceptualized as a complex adaptive system of interdependent health-care domains (Ellis, Churruca, & Braithwaite, 2017), requiring the interaction of mental health, physical health, and social well-being services (Rosenberg & Hickie, 2013). One of Australia’s recent achievements in this regard has been the creation of head- space, Australia’s National Youth Mental Health Foundation, which offers a ‘one-stop shop’ for young people aged 12 to 25 years to address health concerns as well as issues of education and employment (McGorry, Hamilton, Goldstone, & Rickwood, 2016). With over 100 centers in metropolitan, regional and rural areas of Australia, there are large numbers

of consumers using this service. Over 80,000 young people accessed a headspace centre between 2016 and 2017, with approximately 70% classified as having high or very high levels of distress (Hetrick et al., 2017).

Integrated care by means of interprofessional collabora- tion (Braithwaite, 2010), such as that adopted in the head- space model, is considered particularly important when health-care professionals (HCPs) face complex and uncer- tain situations. Drawing on the skills and knowledge of those with diverse backgrounds and expertise provides an opportunity for different ways of thinking when faced with the kinds of uncertain, chronic, complex cases often encountered in MHC delivery (Mauthner, Naji, & Mollison, 1998). This is particularly feasible in integrated care models because professionals are co-located. Uncertainty faced by professionals in MHC may be related to the complex, unpredictable health-care system they are part of, and the complexity of the human body and mind (Ellis et al., 2017). There are different types of professional uncertainty manifesting across a range of situations (Han, Klein, & Arora, 2011), including, but not limited to: decid- ing on unclear diagnoses (termed here as decision uncer- tainty) (Han et al., 2011) and clarifying blurred professional boundaries (termed here as role uncertainty) (Williams & Sibbald, 1999). Such variants are particularly prominent in the domain of MHC given the ambiguity entailed in psy- chiatric diagnoses and clinical guidelines (Dowrick, 2013; Kirk & Kutchins, 1992) and the fact that coordinated care may blur boundaries between professional groups (Edgren & Barnard, 2012). For example, decision uncertainty in

CONTACT Chiara Pomare chiara.pomare@mq.edu.au Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road, Sydney, NSW 2109, Australia Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/ijic.

JOURNAL OF INTERPROFESSIONAL CARE 2019, VOL. 33, NO. 5, 497–503 https://doi.org/10.1080/13561820.2018.1544550

© 2018 Taylor & Francis Group, LLC

MHC may be exacerbated because presentations of the disorder are often highly variable; for example, different cases of major depressive disorder may share a diagnosis but none of the same symptoms (Dowrick, 2013). It is during these complex and uncertain situations that inte- grated youth health care is potentially the most beneficial. However, despite the idealization of integrated care models in MHC such as headspace (McGorry, Purcell, Hickie, & Jorm, 2007), we do not know whether the aspirations for interprofessional collaboration are being realized.

Independent evaluations of headspace have suggested that despite the co-location of different services, commu- nication still occurs within professional silos (Muir et al., 2009) and that the reported mental health of young people using headspace services are mixed, with only “small improvements” (Hilferty et al., 2015) being reported. However, the claims of poor interprofessional collaboration have been based on anecdotal evidence rather than empiri- cal research; a common issue in evaluations of the head- space model (Jorm, 2016). Past empirical research examining interprofessional working in community-based MHC, similar to the headspace model, showed that staff were cohesive and well-connected (Barnett, Hoang, Cross, & Bridgman, 2015). Therefore, the present study aimed to examine how professionals working in integrated youth health care (headspace centres), collaborate in their routine work, and when faced with distinct situations of uncer- tainty. To our knowledge, this is the first study to empiri- cally assess collaborative patterns between headspace staff.

Methods

Prior to the commencement of the study, ethical approval was granted from the Macquarie University Human Research Ethics Committee [HREC ref 5201700297]. Data were collected in July 2017.

Study setting and participants

The study was conducted at two headspace centres in metropo- litan Australia, which we refer to using the pseudonyms Northwestern and Riverside to preserve their anonymity. Participants were staff members employed at either center at the time of the project and were invited to participate via email invitation sent from their center manager. One follow-up remin- der email was sent to staff two weeks later. Inclusion criteria comprised staff (clinical and non-clinical) identified by the cen- ter manager. At the time of the research project, Northwestern and Riverside had 23 and 27 staff members, respectively. This is a typical network size for research taking a social network approach in health-care (Creswick & Westbrook, 2010; Long, Cunningham, Carswell, & Braithwaite, 2013) and specifically, MHC (Barnett et al., 2015). One notable difference between the headspace centres was their duration of operation: Northwestern was established two years prior to data collection, whereas Riverside opened its doors almost a decade prior.

Social network census

A social network census was designed, drawing on and modifying standard social networking questions used in other studies (e.g. Long, Cunningham, & Braithwaite, 2012). A name interpreter was used whereby all persons (actors in social network theory (e.g. Robins, 2015)) within a network of defined boundaries (each headspace centre) were listed on the survey. Participants were asked to rate the frequency and strength of their relationship to each of these colleagues in relation to three specific questions: “Please select those people with whom you have collabo- rated in the last 6 months in your routine work”; “Please select those people whom you would turn to (e.g., to ask for clarity or advice) when you experience role uncer- tainty.”; “Please select those people whom you would turn to when you are unsure in making any kind of decision related to your work at [the centre].” Examples of role uncertainty (“Am I doing this right?” “What is the bound- ary between my role and the next?”) and decision uncer- tainty (clinical diagnoses, treatment, causal explanations) were provided in the preamble to each question. A separate survey was created for each headspace centre; questions were identical apart from the list of staff mem- bers’ names, which participants rated for collaboration. The survey was self-administered online using the survey host- ing website Qualtrics, and took approximately 5–10 minutes for staff to complete.

Statistical analysis

Survey data was collected using social network analysis (SNA). SNA is an important tool in revealing patterns of communication between members and highlighting key players within a defined group (Long & Bishop, 2018; Robins, 2015). Graph patterns (sociograms) of directed collaborative ties were created using Gephi 0.9.2 (Bastian, Heymann, & Jacomy, 2009) and UCInet v.6 (Borgatti, Everett, & Freeman, 2002) was applied for the analysis of network parameters. Network parameters of density, cen- tralization, centrality, and sub-group cohesion were com- puted and used to assess the nature of collaborative activities between staff and operation of the network, and suggest areas for improvement. Number of ties, iso- lates, and network components were also measured in each network for each center (See Table 1 for definitions of key SNA terms). SNA output allowed for the compar- ison of collaborative patterns in the face of different uncertainties and routine work, as well as comparisons between the two headspace centres. To handle any missing responses among centre staff, data on outward (I turn to) and inward ties (This person turns to me) were collected. Inward ties were used in cases where outward ties were not available (non-respondents). This means that when one staff member in the dyad reported a directed tie, the tie is assumed to exist without confirmation from the other staff member.

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Results

Demographics

The response rate was 57% (13/23) for Northwestern and 37% (10/27) for Riverside. A comparison of respondents and non-respondents revealed no significant differences in the representation of professional groups within each net- work (i.e., non-clinicians, such as administrative staff, management; or clinicians: specialist clinicians, intake clinicians): χ2(1, N = 50) = 1.29, p = .26, and gender distribution: χ2(1, N = 50) = 0.45, p = .83. The two head- space centres were similar in their composition of staff, with no significant differences in the representation of professional groups: χ2(1, N = 50) = 0.99, p = 1.00, and gender distribution between the two centers: χ2(1, N = 50) = 1.04, p = .39. Characteristics of respondents are shown in Table 2.

Professional role was divided into four groups: manage- ment (8.3%); intake clinicians (staff that provide initial assessment of clients, assess immediate risk, and determine if admission is required) (20.8%); specialist clinicians (staff that see clients post admission; psychologist, psychiatrist, general practitioner) (45.8%); administrative staff (25.0%). Descriptive statistics revealed that, in the last six months, staff from both centers most frequently reported collabora- tion in terms of taking part in discussion, teleconference or email exchange (91.3%), followed by seeking advice from colleagues (87.0%) and socializing with other staff in the corridor or over lunch (87.0%). The least frequent mode of

collaboration reported was formal supervision (47.8%). All activities of collaboration were reported by all four groups of professionals (intake clinicians, management, specialist clinicians and administrative staff), and for both full-time and part-time staff.

Social network analysis

Collaboration was explored among headspace staff of Northwestern and Riverside, separately. In total, three net- works were examined per centre: collaboration network dur- ing routine work; collaboration when uncertain about the professional role; collaboration when uncertain about the decision. Network parameters are reported in Tables 3–5, and sociograms are presented in Figure 1(a-f). In the socio- grams, each dot represents a staff member and each line indicates a connection defined by the question.

The role uncertainty collaboration networks were con- siderably sparser than networks of routine collaboration and decision uncertainty, as can be seen by the differences in ties reported, density parameters, and reported isolates (Table 3). For example, when collaborating in the face of role uncertainty, only 24% of all possible connections were used among actors of Riverside (Figure 1(e)), whereas, when these same headspace staff were undertaking their routine work or discussing an uncertain decision, 73–76% of possible connections were pursued (Figure 1(d) and (f)). The Riverside network of routine collaborative work had the highest score of density (73%) and centralization (78%), indicating a highly connected network dominated by cen- tral hubs. The most central actors in the network, that is, those that had the most interactions and thus the most influence (Hawe, Webster, & Shiell, 2004), were not specific to one professional group (see Table 4).

Network visualization of sociograms suggested that headspace staff appear to work across professional bound- aries (see Figure 1(a-f)). This was confirmed quantita- tively using a network analysis measure of sub-group cohesion, the E-I Index, which measures the difference

Table 1. Social network analysis terms.

Social network A system of social interactions and personal relationships with interactions between them.

Density Degree of concentration within the network: the number of connections as a proportion of the number of total possible connections. Expressed as a percentage.

Silo A group of people characterized by their limited interaction with external others.

Sub-group cohesion

A sub-group is defined as a group of people directly connected to one another with few connections to other people in the network. Sub-group cohesion is the tendency to which links are within the group rather than to external players; representative of a silo. Sub-group cohesion is commonly measured using the E-I Index.

Centrality A measure to identify which players have the most interaction with others, i.e., the most prominent, “key” players. Centrality of 1 indicates that the actor is interacting with all members of the network.

Centralization Extent to which the network is focused around one or few central people. Expressed as a number between 0 (no centralization) and 1 (complete centralization)

Table 2. Sample characteristics (both headspace centres).

Characteristic Item Frequency (%)

Gender Male 6 (25.0) Female 18 (75.0)

Contract of Employment Full time 11 (45.8) Part time 13 (54.2)

Experience at headspace < 6 months 7 (29.2) 6 months – 2 years 10 (41.7) 2+ years 7 (29.2)

Professional Qualification Psychiatry 2 (8.3) Psychology 7 (29.2) Medicine 2 (8.3) Admin 6 (25.0) Other 7 (29.2)

Table 3. Network parameters of social networks of headspace staff.

Collaboration network

(routine work)

Collaboration when uncertain about professional role

Collaboration when uncertain about decisions

Northwestern (N = 23) Number of ties reported

200 74 159

Number of isolates

0 0 0

centralization 66% 67% 70% Network components

1 1 1

Density 40% 15% 31% Riverside (N = 27)

Number of ties reported

198 51 158

Number of isolates

0 7 0

centralization 78% 46% 59% Network components

1 8* 1

Density 73% 24% 76%

* 1 major component and 7 isolates

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between the number of external and internal-group ties, divided by the total number of ties (Scott, 2012). In this case, an external link is characterized by collaboration across professional groups, for example from management to specialist clinician, whereas internal links are within the same professional group. As can be seen in Table 5, all three networks for each center had a higher tendency towards external ties (approaching 1). This tendency was significant beyond random expectations for the two role uncertainty networks.

Discussion

The present study sought to examine patterns of collaboration between headspace staff working in integrated youth health- care in order to determine whether interprofessional collabora- tion during routine work is being realized and situations of uncertainty are being experienced. Findings revealed that head- space staff collaborate across professional boundaries; however, collaborative patterns differed depending on the particular situation and the headspace centre within which collaboration was assessed. Collaboration in the face of professional role uncertainty was distinguished from collaboration during rou- tine work or decision uncertainty, with role uncertainty net- works showing fewer interactions. The headspace centre that had been in operation for longer demonstrated greater indica- tors of cohesiveness.

Australia’s financial investment in headspace services has been a topic of academic debate (Jorm, 2015), largely criticizing national and international roll-out of headspace initiatives despite the lack of rigorous evaluation (Jorm, 2016). The pre- sent research is, to our knowledge, the first of its kind to examine the patterns of interprofessional collaboration in headspace centres and one of the few in the world using SNA to examine staff relations in mental health settings (Barnett

et al., 2015). The realization of such collaborative care has been called into question given the minimal improvements in head- space client outcomes (Hilferty et al., 2015); however such claims emanate from anecdotal, rather than empirical, evi- dence. Thus, the present research project provides evidence of the interprofessional functioning within headspace centres, highlights areas for improvement and alludes to areas for further study.

Consistent with past headspace research, this study con- firms that there is heterogeneity between headspace centres (Rickwood, Van Dyke, & Telford, 2015). All three Riverside networks had higher density scores than their comparable Northwestern networks. These findings reinforce the impor- tance of context, that headspace centres are more cohesive when they have been operating for longer, as Riverside has been open for over a decade, compared to Northwestern (two years). This implies that the maturity of a service may affect interprofessional collaboration: that with time, networks self- organize and are thus more cohesive and require fewer con- nections. This is consistent with past literature revealing that networks become more collaborative and interactive over time (Long, Hibbert, & Braithwaite, 2016) and is explainable when taking a complexity lens to the health-care system (Braithwaite et al., 2017). Health-care teams, such as those found in integrated health-care (Edgren & Barnard, 2012) and MHC (Ellis et al., 2017), have been described as complex adaptive systems that can adapt and self-organize to complex situations, such as uncertainty (Pype et al., 2017). This phe- nomenon of self-organization means that over time, networks adapt, creating their own local rules. In this case, the Riverside headspace centre has been in operation for enough time to exhibit self-organizing tendencies to create local rules, and thus fewer interactions are required. Policy and planning should endeavor to harness these naturally-occurring charac- teristics of complex systems (i.e. self-organization) (Braithwaite, Runciman, & Merry, 2009), by encouraging the creation of local rules to overcome local uncertainties and other complex issues in a context-specific way.

For both headspace centres, there was a tendency to form ties with staff external to the professional group, rather than internal. The tendency for interprofessional collaboration identified here is inconsistent with previous health-care research (Chan et al., 2017; Cott, 1997). This is because integrated care is a distinct context that requires an integration of mental health, physical health and social well-being services (Rosenberg & Hickie, 2013); thus staff must blur professional boundaries. Such patterns of inter- professional collaboration allow the flow of useful informa- tion (i.e., which role does what) to other members of the network (Braithwaite, 2010; Long, Cunningham, Carswell, & Braithwaite, 2014). This reinforces the importance of context when assessing interprofessional collaboration.

Limitations of this study lie in the dependency on human recollection of events to assess collaboration. When remember- ing many instances of the same experience, such as past colla- boration with colleagues, it is common that separate occurrences are blended together as a prototypical experience (Baranowski, 1988). This risk has been to some extent mitigated in the present social network survey through the list of names and the concrete

Table 4. Freeman’s degree centrality: Players with the highest interaction.

Collaboration network

(routine work)

Collaboration when uncertain

about professional role

Collaboration when uncertain about decisions

Northwestern (N = 23)

(1) M (1.00) (2) M* (1.00) (3) C (0.96) (4) A (0.91)

(1) M (0.96) (2) A (0.77) (3) C (0.55) (4) C (0.46)

(1) M (1.00) (2) M* (1.00) (3) C (0.86) (4) PH (0.77)

Riverside (N = 27)

(1) C (0.87) (2) M* (0.87) (3) A (0.78) (4) C* (0.78)

(1) A (0.50) (2) C (0.39) (3) C (0.26) (4) M (0.22)

(1) M (0.84) (2) C (0.76) (3) C (0.71) (4) MH (0.68)

* Equal to the previous player Key: MH = Mental health clinician, PH = primary health clinician (specialist clinicians); C = intake clinicians (conduct an initial assessment to determine admission); M = management staff; A = administrative staff.

Table 5. E-I index scores: Professional proximity.

Collaboration network (routine work)

Collaboration when uncertain about professional role

Collaboration when uncertain about decisions

0.033 0.726* 0.099 0.095 0.120* 0.032

* Significant (p < .05).

500 C. POMARE ET AL.

delimiter of: “in the last six months”, to aid participants’ recall. Another limitation is the focus of quantity of relationships rather than quality, that is, little is said in SNA about the nature of the information transferred or depth of discussions. Ideally, network analysis should configure a detailed picture of collaboration, including the quality of patterns of collaboration. However, social network surveys in themselves are resource-intensive and burdensome. To add elements of quality may increase respondent fatigue. Further, in this study, inward and outward ties were collected to address issues of non-respondents and provide a maximum representation of the data; however, the findings are limited to the subjectivity of respondents and may leave room for inflation of measurement error (Kossinets, 2006). This is particularly so in the case of the Riverside center where 37% (n = 10) of staff who completed the survey are assumedly representing the network of 27 staff members. Finally, while SNA allows the novel analysis of collaborative patterns among

actors in a specific boundary (headspace staff), little can be said about the efficacy of observed collaborative patterns. Future research must clarify the network parameters that contribute to positive staff and patient outcomes.

Conclusion

With over 100 headspace centres offering integrated youth health-care in Australia, there is national pressure to leverage the investments to improve the burden of the poor mental health of young Australians. Such centers have been criticized for surprisingly not demonstrably improving patient outcomes and having a lack of inter- professional collaboration in an organization that advo- cates coordinated care of co-located professionals; however, such claims are based on anecdotal rather than empirical evidence. Our study of two metropolitan centers

Figure 1. (a-f) Sociograms of routine collaborative work (a, d); collaboration during role uncertainty (b, e); and, collaboration during decision uncertainty (c, f). Nodes represent a staff member, and each line indicates a directed tie. Size of the node is relative to in- and out-degree.

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revealed clear patterns of interprofessional collaboration implying that the goal of integrated care is being realized, and more so in centers that have been in operation for longer. The headspace staff showed a tendency to collabo- rate with colleagues outside of their professional group (rather than inside) in their routine work and when faced with uncertainties of decision and role. Networks were well connected when collaborating in routine work and when faced with decision uncertainty, with decreased interaction during times of role uncertainty.

ORCID

Chiara Pomare http://orcid.org/0000-0002-9118-7207 Janet C Long http://orcid.org/0000-0002-0553-682X Louise A Ellis http://orcid.org/0000-0001-6902-4578 Kate Churruca http://orcid.org/0000-0002-9923-3116 Jeffrey Braithwaite http://orcid.org/0000-0003-0296-4957

References

Asarnow, J. R., Rozenman, M., Wiblin, J., & Zeltzer, L. (2015). Integrated medical-behavioral care compared with usual primary care for child and adolescent behavioral health: A meta-analysis. JAMA Pediatrics, 169(10), 929–937. doi:10.1001/jamapediatrics.2015.1141

Baranowski, T. (1988). Validity and reliability of self report measures of physical activity: An information-processing perspective. Research Quarterly for Exercise and Sport, 59(4), 314–327. doi:10.1080/ 02701367.1988.10609379

Barnett, T., Hoang, H., Cross, M., & Bridgman, H. (2015). Interprofessional practice and learning in a youth mental health service: A case study using network analysis. Journal of Interprofessional Care, 29(5), 512–514. doi:10.3109/13561820.2015.1004042

Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. Paper Presented at the International AAAI Conference on Weblogsand Social Media.

Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for windows: Software for social network analysis. Boston, USA: Analytic Technologies.

Braithwaite, J. (2010). Between-group behaviour in health care: Gaps, edges, boundaries, disconnections, weak ties, spaces and holes. A systematic review. BMC Health Services Research, 10(1), 330. doi:10.1186/1472-6963-10-302

Braithwaite, J., Churruca, K., Ellis, L. A., Long, J. C., Clay-Williams, R., Damen, N., … Ludlow, K. (2017). Complexity science in healthcare- aspirations, approaches, applications and accomplishments: A white paper. Sydney, Australia: Macquarie University.

Braithwaite, J., Runciman, W. B., & Merry, A. F. (2009). Towards safer, better healthcare: Harnessing the natural properties of complex socio- technical systems. BMJ Quality & Safety, 18(1), 37–41. doi:10.1136/ qshc.2007.023317

Chan, B., Reeve, E., Matthews, S., Carroll, P. R., Long, J. C., Held, F., … Hilmer, S. N. (2017). Medicine information exchange networks among healthcare professionals and prescribing in geriatric medicine wards. British Journal of Clinical Pharmacology, 83(6), 1185–1196. doi:10.1111/ bcp.13222

Cott, C. (1997). “We decide, you carry it out”: A social network analysis of multidisciplinary long-term care teams. Social Science & Medicine, 45(9), 1411–1421. doi:10.1016/S0277-9536(97)00066-X

Creswick, N., & Westbrook, J. (2010). Social network analysis of medica- tion advice-seeking interactions among staff in an australian hospital. International Journal of Medical Informatics, 79(6), 116–125. doi:10.1016/j.ijmedinf.2008.08.005

Dowrick, C. (2013). Depression as a culture-bound syndrome: Implications for primary care. British Journal of General Practice, 63, 229–230. doi:10.3399/bjgp13X665189

Edgren, L., & Barnard, K. (2012). Complex adaptive systems for manage- ment of integrated care. Leadership in Health Services, 25(1), 39–51. doi:10.1108/17511871211198061

Ellis, L. A., Churruca, K., & Braithwaite, J. (2017). Mental health services conceptualised as complex adaptive systems: What can be learned? International Journal of Mental Health Systems, 11(1), 43. doi:10.1186/ s13033-017-0150-6

Han, P., Klein, W., & Arora, N. (2011). Varieties of uncertainty in health care: A conceptual taxonomy. Medical Decision Making, 31(6), 828–838. doi:10.1177/0272989X10393976

Hawe, P., Webster, C., & Shiell, A. (2004). A glossary of terms for navigating the field of social network analysis. Journal of Epidemiology & Community Health, 58(12), 971–975. doi:10.1136/jech.2003.014530

Hetrick, S. E., Bailey, A. P., Smith, K. E., Malla, A., Mathias, S., Singh, S. P., … Fleming, T. M. (2017). Integrated (one-stop shop) youth health care: Best available evidence and future directions. The Medical Journal of Australia, 207(10), 5–18.

Hilferty, F., Cassells, R., Muir, K., Duncan, A., Christensen, D., Mitrou, F., … Tarverdi, Y. (2015). Is headspace making a difference to young people’s lives? Final report of the independent evaluation of the headspace program. Sydney, Australia: Social Policy Research Centre, UNSW Australia.

Jorm, A. F. (2015). How effective are ‘headspace’youth mental health services. Aust NZJ Psychiatry, 49(10), 861–862. doi:10.1177/ 0004867415608003

Jorm, A. F. (2016). Headspace: The gap between the evidence and the arguments. Australian and New Zealand Journal of Psychiatry, 50(3), 195–196. doi:10.1177/0004867416632063

Kirk, S. A., & Kutchins, H. (1992). The selling of dsm: The rhetoric of science in psychiatry. New York, NY: Transaction Publishers.

Kossinets, G. (2006). Effects of missing data in social networks. Social Networks, 28(3), 247–268. doi:10.1016/j.socnet.2005.07.002

Long, J. C., & Bishop, S. (2018). Social network research. In P. Liamputtong (Ed.), Handbook of research methods in health social sciences (pp. 1–16). Singapore, Republic of Singapore: Springer.

Long, J. C., Cunningham, F., & Braithwaite, J. (2012). Network structure and the role of key players in a translational cancer research network: A study protocol. BMJ Open, 2(3), e001434. doi:10.1136/bmjopen- 2012-001434

Long, J. C., Cunningham, F., Carswell, P., & Braithwaite, J. (2013). Who are the key players in a new translational research network? BMC Health Services Research, 13(1), 338. doi:10.1186/1472-6963- 13-438

Long, J. C., Cunningham, F., Carswell, P., & Braithwaite, J. (2014). Patterns of collaboration in complex networks: The example of a translational research network. BMC Health Services Research, 14 (1), 225. doi:10.1186/1472-6963-14-225

Long, J. C., Hibbert, P., & Braithwaite, J. (2016). Structuring successful collaboration: A longitudinal social network analysis of a translational research network. Implementation Science, 11(1), 19. doi:10.1186/ s13012-016-0381-y

Mauthner, N., Naji, S., & Mollison, J. (1998). Survey of community mental health teams. Psychiatric Bulletin, 22(12), 733–739. doi:10.1192/pb.22.12.733

McGorry, P. D., Hamilton, M., Goldstone, S., & Rickwood, D. J. (2016). Response to jorm: Headspace–A national and international innova- tion with lessons for redesign of mental health care in australia. Australian and New Zealand Journal of Psychiatry, 50(1), 9–10. doi:10.1177/0004867415624553

McGorry, P. D., Purcell, R., Hickie, I. B., & Jorm, A. F. (2007). Investing in youth mental health is a best buy. Medical Journal of Australia, 187 (7), S5.

Muir, K., McDermott, S., Gendera, S., Flaxman, S., Patulny, R., & Sitek, T. (2009). Independent evaluation of headspace: The national youth mental health foundation: Interim evaluation report. Sydney, Australia: Social Policy Reseach Centre UNSW.

Porter, M. E., & Lee, T. H. (2013). The strategy that will fix health care. Harvard Business Review, 91(10), 1–19.

Pype, P., Krystallidou, D., Deveugele, M., Mertens, F., Rubinelli, S., & Devisch, I. (2017). Healthcare teams as complex adaptive

502 C. POMARE ET AL.

systems: Focus on interpersonal interaction. Patient Education and Counseling, 100(11), 2028–2034. doi:10.1016/j.pec.2017.06.029

Reeves, S., Lewin, S., Espin, S., & Zwarenstein, M. (2010). Interprofessional teamwork for health and social care (Vol. 8). Chichester, UK: John Wiley & Sons.

Rickwood, D., Van Dyke, N., & Telford, N. (2015). Innovation in youth mental health services in australia: Common characteristics across the first headspace centres. Early Intervention Psychiatry, 9(1), 29–37. doi:10.1111/eip.12071

Robins, G. (2015). Doing social network research: Network-based research design for social scientists. London, UK: Sage.

Rosenberg, S., & Hickie, I. (2013). Managing madness: Mental health and complexity in public policy. Evidence Base, 3, 1–19.

Scott, J. (2012). Social network analysis. London, United Kingdom: Sage. Williams, A., & Sibbald, B. (1999). Changing roles and identities in

primary health care: Exploring a culture of uncertainty. Journal of Advanced Nursing, 29(3), 737–745.

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