Primary care networks and team effectiveness: the case of a large-scale quality improvement disparity reduction program.

ABSTRACT Documentation of primary care teams’ involvement in disparity reduction efforts exists, yet little is known about how teams interact or perceive their effectiveness. We investigated how the social network and structural ties among primary-care-clinic team members relate to their perceived team effectiveness (TE), in a large-scale disparity reduction intervention in Israel’s largest insurer and provider of services. A mixed-method design of Social Network Analysis and qualitative data collection was employed. 108 interviews with medical, nursing, and administrative teams of 26 clinics and their respective managerial units were performed and information on the organizational ties, analyzing density and centrality, collected. Pearson correlations examined association between network measures and perceived TE. Clinics with strong intra-clinic density and high clinic–subregional-management density were positively correlated with perceived TE. Clinic in-degree centrality was also positively associated with perceived TE. Qualitative analyses support these findings with teamwork emerging as a factor which can impede or facilitate teams’ ability to design and implement disparity reduction interventions. The study demonstrates that in an organization-wide disparity reduction initiative, cohesive intra-network structure and close relations with mid-level management increase the likelihood that teams perceive themselves as possessing the skills and resources needed to lead and implement disparity reduction efforts. List of abbreviations Team Effectiveness (TE); Clalit Health Services (Clalit); Social Network Analysis (SNA); Quality Improvement (QI); National Health Care Collaborative (NHPC); Tampa Bay Community Cancer Network (TBCCN)


Background
Growing evidence on the difficulty in achieving quality improvement (QI) and reducing health and health care disparities in diverse populations (Chin, 2016;Clarke et al., 2013;Hulscher, Schouten, Grol, & Buchan, 2013), has led to increased attention to the role of health care teams (Brennan et al., 2013;Clark, Quinn, Dodge, & Nelson, 2014;Kaplan et al., 2010;White et al., 2011). Interprofessional teams are increasingly valued for their potential to innovate, solve problems, and implement change (Nembhard & Edmondson, 2006). Research has shown that effective teamwork leads to higher-quality decision making and medical intervention and, in turn, to better patient outcomes (Buljac-Samardzic, Dekker-van Doorn, van Wijngaarden, & van Wijk, 2010;Grumbach & Bodenheimer, 2004;Lemieux-Charles, 2006). Communication and information flow among members, coordination, and engagement of team members as well as establishing shared goals, were found to be positively associated with success of interventions in a range of chronic illness prevention and control strategies and in efforts to improve access to primary care (Gort, Broekhuis, & Regts, 2013;Körner et al., 2016;Quinlan & Robertson, 2010). Nonetheless, despite the important role teams play in developing and implementing QI interventions, the contextual effect of team functioning is not well understood (Braithwaite, Runciman, & Merry, 2009;Brennan et al., 2013;Sims, Hewitt, & Harris, 2015;White et al., 2011).
Social network analysis (SNA) has emerged as a useful tool for mapping and analysing networks of interconnected actors to understand how teams operate within organizational units. SNA characterizes team relations using measures such as the amount and strength of ties among members, the degree to which teams operate closely together, and the degree to which team members are central to the implementation of team efforts. Interestingly, SNA has only been sparsely used to assess how teams work within disparity reduction efforts. For example, Gold and colleagues (Gold, Doreian, & Taylor, 2008) examined the interactions between organizations participating in the National Health Care Collaborative (NHPC) to reduce racial and ethnic disparities and showed that the various health plans participating in the collaborative rarely communicated with one another and that NHPC's sponsor organizations and primary support organizations were an important part of the network's core. Similarly, Luque and colleagues (Luque et al., 2011) used SNA to examine the characteristics of a community-based partnership network focused on reducing cancer disparities among racial-ethnic minority and medically underserved populations in the Tampa Bay Community Cancer Network (TBCCN). Their study identified measures that were associated with the sustainability of the TBCCN and showed the importance of increased interactions of network partners for the collaborative's ongoing work.
This study assessed primary care teams in Israel's largest health care organization, Clalit Health Services (Clalit). In 2009, Clalit launched an organization-wide QI program aimed at reducing gaps between low-performing clinics serving mostly low socioeconomic and minority populations and the general Clalit member population, in a composite measure of seven health and health care indicators: diabetes, hypertension, and lipid control; anemia prevention in infants; and performance of mammography and occult blood tests and of influenza immunizations for the chronically ill (Balicer et al., 2015;Balicer et al., 2011). The program targeted 55 primary care clinics serving approximately 400,000 people (10% of Clalit's population), of mainly economically disadvantaged and minority groups, who were identified as performing poorly on the composite indicators measure. Although the overall organizational goals for disparity reduction and the measurement scheme were set by the central Clalit management, the interventions formulated and their implementation strategy were developed locally at the regional, subregional, and primary-care clinic levels.
We investigated how the organizational structure and social relations among primary-care-clinic team members were associated with their perceptions of effectiveness in leading and implementing disparity reduction interventions to improve the care of disadvantaged populations they serve.

Setting and design
This study employed a mixed qualitative and quantitative convergent design (Guetterman, Fetters, & Creswell, 2015), using semi-structured in-depth interviews, self-rated questionnaires and SNA to investigate the implementation of Clalit's disparity reduction program between 2009 and 2012 (Balicer et al., 2015;Balicer et al., 2011).
Clalit, a non-for profit integrated delivery system organization, is the largest insurer and provider of services in Israel (53% market share). Clalit's decentralized organizational structure comprises (a) 8 regional management units, overseeing and employing the organizational goals for a defined geographical region for all community (primary and specialty care, including all pharmacy, imaging and lab) services in its area; (b) 3-4 subregional managerial units, which serve as middle-management teams, overseeing the operation of clinics (Birken, Lee, & Weiner, 2012); and (c) 5-10 primary care clinics employing a team of physician, nurse administrative employees and pharmacists where an inhouse pharmacy can be found. Clinics operate similarly to Accountable Care Organizations (ACOs) (Filc, 2010). Primary care clinics are organized in teams of physicians, nurses, pharmacists, and administrative employees.
The study was carried out in 26 of the 55 clinics and their respective managerial units, participating in the disparity reduction program, serving 217,306 enrollees in four of the organization's eight geographic regions. Selected clinics were representative of the focus clinics' characteristics including medium-large clinics in rural (n = 19) and urban centers (n = 7) spread around the country, and diverse patient population groups (Jewish Ultra-orthodox = 2, low-socioeconomic peripheral communities = 4, and Arab minorities = 20). The total number of enrollees in the 26 target clinics was 217,306, of whom a majority were of Arab ethnicity (78.1%) and of low socioeconomic status (78.91%) (Balicer et al., 2011;Spitzer-Shohat et al., 2017).

Data collection and measures
Participants were members of the interdisciplinary managerial teams (medical, nursing, and administrative directors) of the 26 clinics of 4 of the organization's regions. These team members also serve as clinicians/administrative staff, taking part in the routine work of the clinics. Additionally, the clinics' associated managerial levels (subregional management for each 2-4 clinics and each region's headquarters) were included. Clinic teams comprised 26 physicians, 26 nurses, 20 administrative heads, five pharmacists and one clinic quality-improvement coordinator (n = 78). The 10 mid-level management, subregional managerial teams included 10 medical directors, seven nursing directors and two administrative directors (n = 19). The four regional management teams included four medical directors, four nursing directors, and two quality-improvement coordinators (n = 10).
The social network boundaries were defined as the actors chosen by the organization to participate in the implementation of the disparity reduction program (Laumann, Marsden, & Prensky, 1989;Wasserman & Faust, 1994). Hence, data were collected from the entire managerial team at each of the three organizational levels (clinic, subregional management, and regional management).
The study was approved by the Committee on Human Studies, Clalit Health Services, Meir Medical Center (authorization no. 172/2011). A data collection session was conducted with each participant by two of the researchers between April and September 2010. The session consisted of two parts: (A) a semi-structured in-depth interview, and (B) the administration of a questionnaire on the network matrices of ties and on TE.

Qualitative phase
Questions in the interview guide relating to the organization's social network and relationships between the different actors were adapted from the work of Wasserman and Faust (Wasserman & Faust, 1994). Questions focused on the intraorganizational relationships between clinic personnel as well as their relationships with other relational network actors in the organization, that is, subregional management, regional management, and collegial clinics.
Interviews were transcribed and analyzed to elicit information on the types of relationships and flow of information between network actors. A key informant was identified in each target clinic. The information provided by the key informant was augmented by information from interviews with the other key clinic personnel. On completion of the interviews, we conducted a member-check by sending a summary of the interviews to each target clinic for validation of the information elicited as well as additional comments (Sandelowski, 2008).

Quantitative measures
Social network data were obtained through a questionnaire administered to study participants which included a roster for identifying network ties in each participant's advice network. The roster listed the relevant organizational positions at all three managerial levels (medical, nursing, administrative, and pharmacy or other personnel were relevant in each clinic, subregional, and regional management team). Respondents were asked to rank "To what extent do you consult with members of your organization on matters pertaining to the disparity reduction quality improvement initiative?" (Borgatti, Everett, & Johnson, 2013;Valente, 2010;Zohar & Tenne-Gazit, 2008) on a scale from 1 (very little) to 5 (a lot).
Data on the types and intensity of ties were used to generate matrices of ties between network actors (nodes) and to construct both structural and relational network measures. Structural network measures were constructed to assess density (as the sum of the values of all ties divided by the number of possible ties) for within-clinic networks, clinic-subregional-management networks, clinic-regional networks and overall regional networks (including all clinic, subregional and regional teams) (Hanneman & Riddle, 2005).
Relational measures were constructed to address the type of relationship between the differential network nodes and included measures of network centralization, group centrality, and group betweenness centrality. Centralization indicates the degree of asymmetry in the network, with some actors having more ties than others (Freeman, 1979). Group degree centrality calculates the number of non-group nodes that are connected to group members. Multiple ties to the same node are counted only once (Everett & Borgatti, 1999). As the network data are directed, the group centrality measure distinguishes between ties coming from outside into the group, or in-degree centrality, and ties from the group to other members in the network, or out-degree centrality. Thus, group centrality is a quantitative measure of a group's social activity in the organizational network.
Group betweenness centrality relates to the extent to which a group may be the point of connection to other actors in the network (Wasserman & Faust, 1994). This measure indicates the proportion of ties connecting pairs of non-group members that pass through the group (Everett & Borgatti, 1999). A clinic with high betweenness can increase its social capital and improve TE by serving as a bridge between different actors in the network who have complementary sources of information, that is, bridging structural holes, in turn increasing the clinic's resources (Burt, 2000).
To assess perceived TE, we asked clinic participants (n = 78) to complete a validated questionnaire which includes 22 items addressing different aspects of TE (Shortell et al., 2004). The four domains of the perceived TE questionnaire include (1) team effectiveness, which addresses whether the team had the necessary information, authority and autonomy to implement the disparity reduction program; (2) team skills, or the perceived ability to administer changes; (3) participation and goal agreement, which reflects team cohesion; and (4) organizational support, which addresses not only the attainment of resources but also work-related incentives (Shortell et al., 2004). All items are measured on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). A composite score of perceived TE which includes all 22 items was constructed based on the aggregated mean score of all clinic team members for each of the 26 clinics (Kirkman, Tesluk, & Rosen, 2001;Shortell et al., 2004). Internal reliability of the questionnaire measured by Cronbach alphas ranged from 0.85 to 0.95 (Shortell et al., 2004).
To stratify clinics by perceived TE level, we categorized clinics into tertiles (lowest-, medium-and high-performing TE clinics), according to the distribution of TE scores to the following cut points: 3.68 or below, 3.69 to 4.02, and above 4.02, respectively.

Analysis
Qualitative data was analyzed using thematic analysis to characterize the types and strength of ties between network members (Stewart, Makwarimba, Barnfather, Letourneau, & Neufeld, 2008). Quantitative SNA analysis was conducted using UCINET for Windows, Version 6 (Borgatti, Everett, & Freeman, 2002). To depict the relationship between the structural network representation and perceived TE, we used a graphic representation known as a sociogram (Freeman, 2000). In a sociogram, each node indicates an individual or actor in the network and each line indicates a connection. To visualize network data, we used the software program NetDraw (Borgatti, n. d.). The matrices analyzed in the UCINET software were transferred into NetDraw. Each of the network nodes or actors was assigned a shape according to its organizational unit attribute. A circle represents personnel working in the target clinics, subregional managers are represented by squares, and regional managers are represented by triangles. Clinics were also assigned a color according to their perceived team effectiveness level, with red indicating low perceived TE, and green, high perceived TE. Last, ties were assigned a color according to the intensity of the connection: low intensity (1-2) is light blue, medium intensity (3) is orange, and high intensity (4-5) is dark blue. Note that isolates -nodes that were not reported to have connections to other members in the network-were also indicated for each of the four regions in the upper left corner of each network. The networks constructed by NetDraw were visually inspected for trends and distributions.
To examine the association between structural and relational network characteristics, and perceived TE, we conducted an exploratory analysis using the non-parametric Kendall's Tau.

Results
Regional networks differ in the amount and strengths of ties among their members as depicted in the primary care teams' advice network for exchanging information related to the disparity reduction and their perceived TE levels. As is shown in Figure 1, many of the nodes in the network are connected to others in similar patterns-in each clinic members are usually connected with one another and with each of the subregional and regional managerial members. Region C is an exception, as none of the clinic members reported ties with the regional management. The different color lines, which illustrate the strength of the ties, exemplify the variations in ties among clinic members and between primary care clinic members and their respective managerial teams. For example, in region B, all team members of clinic b5 have strong ties among them. Actors 14 and 16 have strong ties (dark blue) with all three members of the subregional management (black squares) and with all members of the regional management (black triangles). Finally, in region D, two clinics (d3 and d8) present unique patterns, with no connections among members and almost none with subregional and regional managements.
The figure also presents perceived TE ratings of clinics. Clinics in regions B and C all had a high perceived TE level (green) with the exception of one medium-rated TE clinic (yellow). Regions A and D presented a mix of perceived TE rated clinics, including clinics with low perceived TE (red). Clinics with a high level of perceived TE were also characterized by strong ties among clinic actors, as can be seen in clinic b2, and/or strong ties to subregional management (clinic d1). Clinics with low perceived TE were characterized by medium to low tie strength between team members and/or subregional management (clinic a4).
Table 1 provides metrics that describe the four regional networks' characteristics. Network density values ranged from a low of 0.26 (region D) to a high of 0.57 (regions B and C), indicating a difference in the level of interaction among members of each network. All four networks present relatively low levels of in-degree and out-degree centralization, and region B has the highest levels of the four (weighted average in-degree: 1.93 [range 1-5]; weighted average out-degree: 1.38 [range 1-5]), reflecting stronger ties between clinics and subregional and regional management team members than in other regions.
Clinics differed in their clinic density and in their clinicsubregional management and clinic-regional management density. The mean density of clinic teams was highest in district C (3.80) and lowest in district D; the average clinic density was 2.77. Clinic-subregional management and clinicregional management density were highest in district B (1.06 and 0.54, respectively) and lowest in district C (0.64 and 0.00, respectively).
Clinics also differed in their relational measures. Clinic indegree group centrality ranged from 0, i.e., no incoming ties, to 0.61 (clinic d3). Clinics' group betweenness centrality was highest in clinic b1 scoring 11. Clinics in region C all had a betweenness centrality score of 0, suggesting the lack of unique ties with actors outside the clinic team.
Average clinic perceived TE levels ranged from a low of 3.05 in clinic d3 to a high of 4.44 in clinic b4. Regions A and

Clinics with a low perceived TE score
The prevalent network pattern in clinics with low perceived TE (below 3.69) was overall low density, characterized by low density among clinic members (an average clinic density score below 3.19), low clinic-subregional-management density (below 0.84), and low clinic-regional density (below 0.26) (clinics a3, a4, a6, d3, and d4). This pattern is depicted in the following quotes: "Our performance is not good . . . the nurse and administrative staff are not working together, there is no division of labor . . . if I don't invite [patients] then no one else does" [Doctor, clinic d4]. "They do not help out with the resources we need. We have a big problem with access to mammography screening, we've repeatedly requested [from subregional management] to bring to the clinic the mobile screening unit. They [regional management] do not have a plan for how to address the issue of mammography. I care as a clinic director but they also have to make sure everything is working" [Clinic director, physician, clinic d4].
For the three remaining low-perceived-TE clinics there was no distinct pattern of network ties (a8, a9, and d7). Nonetheless, interviews revealed perceived barriers to effective teamwork centered around organizational support of either subregional or regional management. As a clinic director (clinic a9 said): "They [management] do not understand our situation. We work with physicians who operate independent clinics. These doctors do not always understand what the management asks us to do . . . They (the subregional and regional managements) need to go and visit these clinics instead of putting the pressure on us, sending letters to us on poor performance." Clinics with a high perceived TE score Two patterns were identified for clinics with high levels of TE: (a) clinics with an overall medium to high intra-clinic, clinicsubregional and clinic-regional management density; and (b) clinics with low intra-clinic density. Clinics with high levels of TE and overall medium or strong density measures were a2, b4, c1, c3, and d1. Clinic members stated the importance of inner team connectivity as a key factor in successful implementation of the program: "The key to success is working hard and raising awareness of team members. You have team members from different health sectors that are not usually connected to one another and you have to connect them, to explain to them the importance of [quality improvement] indicators, let them see the data, keep them informed" [Clinic director, physician, clinic b4].
These clinics also testified to a close working relationship with subregional management, together addressing barriers affecting the implementation of the program: "We sat together with the subregional medical director and nursing director to try and think how we can culturally tailor the intervention to the patients. We came up with several ideas such as the information pamphlets on the importance of iron supplements for preventing anemia in infants using culturally tailored messages" [Clinic director, clinic d1]. High TE levels but low intra-clinic density are apparent in clinics a7, b2, b3, and b5. These clinics had a relatively high level of density with subregional management (ranging from 1.05-1.14). Members in these clinics attributed much of their success to the support of subregional management and to the resources they received for improving both knowledge and skills in improving the care in areas the program focused on: "Subregional management helped us with training, they convinced team members to attend a 'quality improvement school' or other relevant workshops" [Clinic director, clinic b5]. "He [medical director, subregional management] puts a lot of pressure on us but also helps us by explaining the different indicators, pulling out lists [of relevant patients]" [Clinic director, clinic a7]. Furthermore, members of these clinics also stated the importance of the creation of a shared organizational goal: "It was important [for management] to include the clinic team members in the planning process and explain the goal, what is our 'northern star'" [Nursing director, clinic b2].
Interestingly, two of the nine clinics with high TE scores had a new clinic director (a2 and b5). This situation was described as a facilitator and an opportunity for building strong teamwork and communication. "The first thing I did, was to work on strengthening and connecting the team. I even asked the region not to work on improving the indicators in the first months but on stabilizing the clinic team. Only when I felt that we can work as a team, I asked someone from the region to come and train us on the specifics of the program" [Clinic director, clinic b5].

Clinics with a medium perceived TE score
Intermediate TE level clinics a1, a5, b1, d2, d5, d6 and d8 (TE scores ranging from 3.69-4.02; nodes in yellow) varied in their clinic density measure from low to medium (2.92-3.75). However, all of these clinics scored high on their relational network measure of clinic in-degree centrality (highest rating in each network), testifying to the support of subregional management. Interviews conducted with the subregional management personnel depicted the significant investment of time in working with clinic directors to improve performance: "I met weekly with the clinic directors to try and think together how to improve performance. I try to think out-ofthe-box, for example challenging the clinic directors in an internal contest among clinics awarding lunch to the clinic's team that improved performance and achieved the periodic goal" [Subregional management director, node 107]. "We understood that we needed to invest more than the regular resources, that we have to find the right support for the clinic. After much discussion we decided to provide team training for dealing with patients' resistance to change and motivational counseling, this included lectures, workshops . . . we also assisted them with identifying all patients whose chronic illness was not under control and consulted them to better understand what the clinic has done and what might be done" [Subregional management director, node 127].
For two of the remaining medium-TE-level clinics (a10 and c2), a distinct pattern of network ties could not be identified. However, qualitative analysis alluded to the difference in team members' perceptions of the possible reasons for a medium level of perceived TE. Interviews with team members of clinic a10 attested to the team's effort to change management's perception of them from a low-performing to a high-performing clinic: "We couldn't accept the fact that we were perceived as a poor performing clinic. We met together [the clinic team] and worked out what each of us is going to do. How to do everything we can to change our low status. I called up patients, the doctor went out to the community to elderly people that couldn't come to the clinic. . . . We worked hard together" [Administrative director, clinic a10].
Interestingly, clinic c2 interviews described internal team problems between key clinic personnel as a reason for their poor performance: "Not everyone is doing their job, it can't all be the responsibility of the nurses, where are the doctors? What are they doing to help out?" [Nursing director, clinic c2]. That clinic members did not have close working ties is also evident from their relatively low clinic-density score (Table 1).

Kendall's tau analysis between network measures and perceived TE
The results of the exploratory correlational analysis, presented in Table 2, between network measures and TE show that. clinic-subregional-management density was found to be positively correlated with perceived TE (p < 0.05). Analysis of relational network measures found that clinics' in-degree centrality was positively correlated with TE (p < 0.05). Nonetheless, clinic density, out-degree centrality as well as clinic group betweenness centrality were not found to be correlated with perceived TE of clinic teams.

Discussion
The current study aimed to investigate how organizational structure and social relations are associated with perceived TE in primary care clinics participating in a QI disparity reduction program. We found that network structure, specifically clinic-subregional-management density, is positively correlated with perceived TE and is reported by primary care team members as central to the implementation of the program (see Table 2).
In line with the findings of Birken and colleagues (Birken, Lee, Weiner, Chin, & Schaefer, 2013), as depicted from the qualitative data, we found the role of middle management, that is, subregional management, to be a critical contextual factor associated with perceived TE. Middle management was seen as a source of support by directly providing information and training and by facilitating clinic team members' ability to understand and assimilate the organizational goal. This finding confirms the importance of the role of middle management in health care organizations in an era of downsizing managerial positions and flattening organizational hierarchies (Birken et al., 2013). The unique contribution of middle managers lies in their ability to address the contextual factors affecting the implementation in each of their subordinate units by bridging information gaps that might otherwise impede attainment of the organizational goal through managing the demands associated with the implementation process, aligning incentives, transcending professional barriers, and identifying ways to promote implementation (Birken et al., 2012). This might be especially important within the context of a disparity reduction program, in which tailoring unique solutions to each clinics' special population and geographic area is imperative. Interestingly, intra-clinic density was not found to be significantly correlated with perceived TE. However, qualitative analysis does show that conflicts between team members about their roles within the disparity reduction initiative were apparent in teams with low cohesiveness and a sense of low ability to reach the organizational goal. Similarly, Benzer and colleagues (Benzer et al., 2013), in their qualitative investigation of contextual organizational factors that influence the implementation of quality improvement initiatives, found that collaboration among intra-organizational team members was crucial for facilitating the implementation of the intervention.
overall network centralization was moderately low for the studied regions, indicating a relatively even distribution of information among members. This finding suggests that the organizational structure is strong, as the implementation of the program is not dependent on key players. Sparrowe and colleagues (Sparrowe, Liden, Wayne, & Kraimer, 2001) claimed that decentralized networks foster interdependence, which in turn encourages cooperation. Conversely, when programs are highly dependent on key actors and responsibility is not evenly distributed within the organization, networks may collapse as a result of personnel changes (Cunningham et al., 2012;Lewis, Baeza, & Alexander, 2008;Weenink, van Lieshout, Jung, & Wensing, 2011). Furthermore, we did not find a significant correlation between clinics' betweenness centrality and perceived TE. Similarly, Jippes and colleagues (Jippes et al., 2010) reported the lack of effect of betweenness centrality on adoption of a new program. The study found that the lack of betweenness centrality of actors alluded to the importance of strong ties in creating a homophilous group as well as reinforcing communication among team members.
The current study has several limitations. In line with classic approaches to network studies, it focuses on describing structural features of a network at a specific point and does not assess change over time (Scott, 2012). However, the use of SNA in the current study allows us to understand the barriers to and\or facilitators of the diffusion and implementation of the disparity reduction initiative and offers actionable insight into issues of perceived TE (Meltzer et al., 2010). Another limitation has to do with the quality of interaction among members, in other words, it is not clear whether intra-clinic network ties are strong as a result of daily interactions and not necessarily because of joint work on the disparity reduction program (Meltzer et al., 2010). In this study we tried to address this limitation by using comprehensive mixed methodology in network analysis. Our comprehensive assessment included interviews with 108 personnel directly involved in implementing the intervention. The combination of qualitative and quantitative methods enabled us not only to gain an understanding of the matrices of ties (i.e., quantitative methods) but also to explore the meaning of the relational ties between actors (Edwards, 2010). Additionally, we assessed perceived TE which is a subjective assessment of the team's capacity to undertake work relevant to the organizational goal and not an objective measure. Nonetheless, it is an accepted measure combining various facets of team capabilities such as members' knowledge, skills, perceived organizational support and has been widely used in the quality improvement literature (Chaudoir, Dugan, & Barr, 2013;Kaplan et al., 2010).

Conclusion
This study showed that network structure and ties among primary care team members, working to improve the health and health care of their disadvantaged population groups, are related to the perceptions of their effectiveness. Our results show that strong intra-clinic ties as well as support from subregional management are key factors in clinics with high TE assessment. Understanding the interplay between the organizational network, information flow, and team outcomes is important for organizations aiming to implement a disparity reduction initiative to attain success.