Health Care Quality and Disparities

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ORIGINAL ARTICLE

Primary care networks and team effectiveness: the case of a large-scale quality improvement disparity reduction program S. Spitzer-Shohat a,b, M. Goldfrachtc, C. Keyc, M. Hoshend, R. D. Balicerd,e, and E. Shadmid,f

aDepartment of Population Health, Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel; bCenter for Health and the Social Sciences, University of Chicago, IL, USA; cClalit Community Division, Clalit Health Services, Tel Aviv, Israel; dClalit Research Institute, Chief Physician’s Office, Clalit Health Services, Tel Aviv, Israel; eEpidemiology Department, Faculty of Health Sciences, Ben-Gurion University, Beer-Sheva, Israel; fFaculty of Social Welfare and Health Sciences, University of Haifa, Beer-Sheva, Israel

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, cohe- sive 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)

ARTICLE HISTORY Received 7 January 2018 Revised 27 September 2018 Accepted 29 September 2018

KEYWORDS Quality Improvement; Disparities; Social Network Analysis; Perceived Team Effectiveness

Background

Growing evidence on the difficulty in achieving quality improve- ment (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 team- work leads to higher-quality decision making and medical inter- vention 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, coordi- nation, 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 con- textual 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 parti- cipating 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

CONTACT S. Spitzer-Shohat [email protected]; [email protected] Department of Population Health, Azrieli Faculty of Medicine, Bar- Ilan University, 8 Henrietta Szold St, Safed, 1311502 Israel 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, 472–480 https://doi.org/10.1080/13561820.2018.1538942

JOURNAL OF INTERPROFESSIONAL CARE 2019, VOL. 33, NO. 5, 472�480 https://doi.org/10.1080/13561820.2018.1538942

ORIGINAL ARTICLE

S. Spitzer-Shohat see footnote a and b

Goldfracht see footnote c C. Key see footnote c

M. Hoshend, see footnote d

R. D. Balicer see footnote d and e

and E. Shadmid, see footnote d and f

ABSTRACT ARTICLE HISTORY Received 7 January 2018 Revised 27 September 2018 Accepted 29 September 2018

KEYWORDS Quality Improvement; Disparities; Social Network Analysis; Perceived Team Effectiveness

Growing evidence on the difficulty in achieving quality improvement (QI) and reducing health and health care disparities in diverse populations (Chin, 2016-see page 8; Clarke et al., 2013-see page 8; Hulscher, Schouten, Grol, & Buchan, 2013-see page 8), has led to increased attention to the role of health care teams (Brennan et al., 2013-see page 8; Clark,Quinn, Dodge, & Nelson, 2014-see page 8; Kaplan et al., 2010-see page 8; White et al., 2011-see page 9). Interprofessional teams are increasingly valued for their potential to innovate, solve problems, and implement change (Nembhard & Edmondson, 2006-see page 8). Research has shown that effective team- work 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-see page 8; Grumbach & Bodenheimer, 2004-see page 8; Lemieux-Charles, 2006-see page 8). 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-see page 8; K￶rneretal., 2016-see page 8; Quinlan &

Robertson, 2010-see page 8). 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-see page 8; Brennan et al., 2013-see page 8; Sims, Hewitt, & Harris, 2015-see page 9; White et al., 2011-see page 9).

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-see page 8) 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

CONTACT S. Spitzer-Shohat [email protected]; [email protected] Department of Population Health, Azrieli Faculty of Medicine, Bar- Ilan University, 8 Henrietta Szold St, Safed, 1311502 Israel

Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/ijic.

https://doi.org/10.1080/13561820.2018.1538942

https://crossmark.crossref.org/dialog/?doi=10.1080/13561820.2018.1538942&domain=pdf&date_stamp=2019-09-27

http://orcid.org/0000-0001-9656-0034

mailto:[email protected]

http://www.tandfonline.com/ijic

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 sus- tainability of the TBCCN and showed the importance of increased interactions of network partners for the collabora- tive’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 ser- ving mostly low socioeconomic and minority populations and the general Clalit member population, in a composite measure of seven health and health care indicators: diabetes, hyperten- sion, 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 manage- ment, 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.

Methods

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 ques- tionnaires 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 organiza- tion, is the largest insurer and provider of services in Israel (53% market share). Clalit’s decentralized organizational structure comprises (a) 8 regional management units, over- seeing 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 adminis- trative 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 popu- lation 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 qual- ity-improvement coordinator (n = 78). The 10 mid-level management, subregional managerial teams included 10 med- ical 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 implementa- tion 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 manage- ment, and regional management).

The study was approved by the Committee on Human Studies, Clalit Health Services, Meir Medical Center (author- ization 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 intra- organizational relationships between clinic personnel as well as their relationships with other relational network actors in

JOURNAL OF INTERPROFESSIONAL CARE 473

organizations and primary support organizations were an important part of the network�s core. Similarly, Luque and colleagues (Luque et al., 2011-see page 8) 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 sus- tainability of the TBCCN and showed the importance of increased interactions of network partners for the collabora- tive�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 ser- ving 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-see page 7 ; Balicer et al., 2011-see pg 7). 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, sub regional, and primary-care clinic levels.

This study employed a mixed qualitative and quantitative convergent design (Guetterman, Fetters, & Creswell, 2015-see pg 8), 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-see pg 7; Balicer et al., 2011-see pg 7).

Clalit, a non-for profit integrated delivery system organiza- tion, is the largest insurer and provider of services in Israel (53% market share). Clalit�s decentralized organizational structure comprises (a) 8 regional management units, over- seeing 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-see pg 7); and (c) 5�10 primary care clinics employing a team of physician, nurse adminis- trative employees and pharmacists where an inhouse

pharmacy can be found. Clinics operate similarly to Accountable Care Organizations (ACOs) (Filc, 2010-see page 8). 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 popu- lation 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, ofwhom a majority were of Arab ethnicity (78.1%) and of low socioeconomic status (78.91%) (Balicer et al., 2011-see page 7; Spitzer- Shohat et al., 2017-see page 9).

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-see page 8; Wasserman & Faust, 1994-see page 9). Hence, data were collected from the entire managerial team at each of the three organizational levels (clinic, sub regional management, and regional management).

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-see page 9). Questions focused on the intra- organizational 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 informa- tion 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 infor- mant 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 informa- tion 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 gen- erate matrices of ties between network actors (nodes) and to construct both structural and relational network measures. Structural network measures were constructed to assess den- sity (as the sum of the values of all ties divided by the number of possible ties) for within-clinic networks, clinic–subregio- nal-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 central- ity calculates the number of non-group nodes that are con- nected 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 orga- nizational 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 mem- bers 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 per- ceived ability to administer changes; (3) participation and goal agreement, which reflects team cohesion; and (4) organiza- tional 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 char- acterize 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 struc- tural network representation and perceived TE, we used a gra- phic 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 net- work data, we used the software program NetDraw (Borgatti, n. d.). The matrices analyzed in the UCINET software were trans- ferred 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, subre- gional managers are represented by squares, and regional man- agers 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 rela- tional network characteristics, and perceived TE, we

474 S. SPITZER-SHOHAT ET AL.

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-see page 8)

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-see page 9; Valente, 2010-see page 8; Zohar & Tenne-Gazit, 2008-see page 9) 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). see page 8.

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 central- ity calculates the number of non-group nodes that are con- nected 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 orga- nizational 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) see page 9. This measure indicates the proportion of ties connecting pairs of non-group mem- bers that pass through the group (Everett & Borgatti, 1999)see page 8. 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, page 8).

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-see page 8). 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-see page 8). 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-see page 8; Shortell et al., 2004-see page 8). Internal reliability of the questionnaire measured by Cronbach alphas ranged from 0.85 to 0.95 (Shortell et al., 2004-see page 8).

Qualitative data was analyzed using thematic analysis to characterize the types and strength of ties between network members (Stewart, Makwarimba, Barnfather, Letourneau, & Neufeld, 2008-see page 8). Quantitative SNA analysis was conducted using UCINET for Windows, Version 6 (Borgatti, Everett, & Freeman, 2002-see page 9). To depict the relationship between the structural network representation and perceived TE, we used a graphic representation known as a sociogram (Freeman, 2000-see page 8). 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.-see page 8). 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 man- agers 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 rela- tional 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 mem- bers 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 varia- tions 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 man- agement (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 regio- nal 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 manage- ment (clinic d1). Clinics with low perceived TE were char- acterized 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 sub- regional and regional management team members than in other regions.

Clinics differed in their clinic density and in their clinic– subregional 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 clinic– regional 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 in- degree 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

Region A Region B

Region C Region D

Clinic b1

Clinic b2

Clinic b3

Clinic b4

Clinic b5

Clinic c2

Clinic c3

Clinic c1

Clinic d1

Clinic d4

Clinic d2

Clinic d8

Clinic d7

Clinic d6

Clinic d5

Clinic d3

Clinic a1

Clinic a2

Clinic a10

Clinic a4

Clinic a5

Clinic a6

Clinic a7

Clinic a8

Clinic a9

Clinic a3

Figure 1. Perceived Team Effectiveness Level by Region.

JOURNAL OF INTERPROFESSIONAL CARE 475

conducted an exploratory analysis using the non-parametric Kendall�sTau.

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 see page 4), 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 sub regional 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 varia- tions 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 sub regional 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 sub regional and regional managements.

Table 1, page 5, 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 sub- regional and regional management team members than in other regions.

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

D had the lowest overall average TE score (mean: 3.76, s.d.: 0.30; mean: 3.76, s.d.: 0.43, respectively), and region B had the highest average TE score, at 4.27 (s.d.:0.17).

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, clinic– subregional 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 imple- mentation 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].

Table 1. Network metrics for combined interview and survey network.

Region and clinic Network density

Clinic–regional- management density,

mean (SD)

Clinic–subregional- management

density Clinic density

Group centrality in-

degree

Group centrality out-degree

Group betweenness centrality

Team effectiveness

Region A 0.35 0.22 (0.15) 0.89 (0.29) 3.36 (0.72) 0.76 1.12 2.27 (2.18) Clinic a1 0.05 0.82 3.75 0.18 0.34 3.20 3.96 Clinic a2 0.23 0.55 4.33 0.11 0.21 1.29 4.16 Clinic a3 0.07 0.38 2.67 0.11 0.20 0.29 3.54 Clinic a4 0.21 0.83 2.56 0.09 0.20 2.43 3.39 Clinic a5 0.54 1.00 3.58 0.16 0.34 1.19 3.69 Clinic a6 0.25 0.71 3.00 0.16 0.35 2.35 3.62 Clinic a7 0.29 1.05 2.17 0.13 0.22 0.50 4.28 Clinic a8 0.32 1.07 3.33 0.11 0.21 0.75 3.65 Clinic a9 0.24 1.43 4.50 0.11 0.22 7.75 3.43 Clinic a10 0.00 1.05 3.75 0.04 0.06 2.92 3.85 Region B 0.57 0.54 (0.24) 1.06 (0.13) 3.18 (0.36) 1.93 1.38 4.40 (4.14) Clinic b1 0.16 0.84 3.33 0.097 0.19 11.00 4.02 Clinic b2 0.79 1.09 2.92 0.07 0.19 1.58 4.33 Clinic b3 0.50 1.07 3.00 0.06 0.22 6.00 4.36 Clinic b4 0.66 1.16 3.75 0.07 0.19 1.58 4.44 Clinic b5 0.57 1.14 2.92 0.07 0.19 1.83 4.17 Region C 0.57 0.00 (0.00) 0.64 (0.24) 3.80 (0.89) 1.58 0.96 0.00 Clinic c1 0.00 0.88 4.83 0.00 0.20 0.00 4.15 Clinic c2 0.00 0.41 3.25 0.00 0.21 0.00 3.83 Clinic c3 0.00 0.62 3.33 0.00 0.20 0.00 4.10 Region D 0.26 0.24 (0.10) 0.71 (0.30) 2.77 (1.47) 1.28 0.74 2.35 (2.47) Clinic d1 0.24 1.14 4.67 0.44 0.12 7.17 4.37 Clinic d2 0.18 0.86 2.92 0.46 0.09 1.83 3.98 Clinic d3 0.14 0.27 0.25 0.61 0.30 3.20 3.05 Clinic d4 0.25 0.38 0.92 0.58 0.30 3.20 3.42 Clinic d5 0.18 0.59 3.42 0.52 0.36 0.00 3.99 Clinic d6 0.32 0.98 3.25 0.52 0.30 3.40 3.89 Clinic d7 0.45 0.62 3.82 0.56 0.29 0.00 3.40 Clinic d8 0.18 0.86 2.92 0.50 0.00 0.00 3.98 Overall average (SD) 0.26 (0.21) 0.84 (0.29) 3.20 (1.01) 0.22 (0.21) 0.22 (0.09) 1.71 (1.15)

476 S. SPITZER-SHOHAT ET AL.

Region and clinic Network density Clinic�regional- management density, mean (SD)

Clinic�subregional- management density

Clinic density Group centrality in- degree

Group centrality out-degree

Group betweenness centrality

Team effectiveness

Region A Clinic a1 Clinic a2 Clinic a3 Clinic a4 Clinic a5 Clinic a6 Clinic a7 Clinic a8 Clinic a9 Clinic a10 Region B Clinic b1 Clinic b2 Clinic b3 Clinic b4 Clinic b5 Region C Clinic c1 Clinic c2 Clinic c3 Region D Clinic d1 Clinic d2 Clinic d3 Clinic d4 Clinic d5 Clinic d6 Clinic d7 Clinic d8 Overall average (SD)

D had the lowest overall average TE score (mean: 3.76, s.d.: 0.30; mean: 3.76, s.d.: 0.43, respectively), and region B had the highest average TE score, at 4.27 (s.d.:0.17).

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.�

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 con- vinced 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 direc- tor, 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 rela- tional network measure of clinic in-degree centrality (highest rating in each network), testifying to the support of subregio- nal 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-of- the-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 motiva- tional 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 rea- sons 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 posi- tively correlated with perceived TE (p < 0.05). Analysis of relational network measures found that clinics’ in-degree cen- trality 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, speci- fically 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

Table 2. Relationship between social network and team effectiveness.

Network measures Team effectiveness Kendall’s Tau (p

value)

Clinic density 0.493 (0.097) Clinic–subregional management density

0.375** (0.008)

Clinic–regional management density 0.153 (0.279) Clinic in-degree centrality 0.363* (0.010) Clinic out-degree centrality 0.124 (0.387) Clinic betweenness centrality 0.141 (0.319)

*p < 0.05; **p < 0.01

JOURNAL OF INTERPROFESSIONAL CARE 477

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 rea- sons 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].

In line with the findings of Birken and colleagues (Birken, Lee, Weiner, Chin, & Schaefer, 2013-see page 7), as depicted from the qualitative data, we found the role of middle management, that is, sub regional 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 find- ing confirms the importance of the role of middle manage- ment 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 mana- ging the demands associated with the implementation pro- cess, 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 geo- graphic area is imperative.

Interestingly, intra-clinic density was not found to be sig- nificantly 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 influ- ence 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 pro- grams 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 meth- odology in network analysis. Our comprehensive assessment included interviews with 108 personnel directly involved in implementing the intervention. The combination of qualita- tive and quantitative methods enabled us not only to gain an understanding of the matrices of ties (i.e., quantitative meth- ods) 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 litera- ture (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 dispar- ity reduction initiative to attain success.

Funding

This work was supported by the Israel Institute for Health Policy Research [m/4/2011]

ORCID

S. Spitzer-Shohat http://orcid.org/0000-0001-9656-0034

References

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Balicer, R. D., Shadmi, E., Lieberman, N., Greenberg-Dotan, S., Goldfracht, M., Jana, L., . . . Jacobson, O. (2011). Reducing health disparities: Strategy planning and implementation in Israel’s largest health care organization. Health Services Research, 46(4), 1281–1299. doi:10.1111/j.1475-6773.2011.01247.x

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478 S. SPITZER-SHOHAT ET AL.

and training and by facilitating clinic team members� ability to understand and assimilate the organizational goal. This find- ing confirms the importance of the role of middle manage- ment 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 mana- ging the demands associated with the implementation pro- cess, 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 geo- graphic 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-see page 7), 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-see page 9) claimed that decentralized networks foster interdependence, which in turn encourages cooperation. Conversely, when pro- grams 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-see page 8; Lewis, Baeza, & Alexander, 2008-see page 8; Weenink, van Lieshout, Jung, & Wensing, 2011-see page 9). Furthermore, we did not find a significant correlation between clinics� betweenness centrality and perceived TE. Similarly, Jippes and colleagues (Jippes et al., 2010-see page 8) 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-see page 8). 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-see page 8). 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-see page 8). 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 meth- ods) but also to explore the meaning of the relational ties between actors (Edwards, 2010-see page 8). 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-see page 8; Kaplan et al., 2010 see page 8).

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Sims, S., Hewitt, G., & Harris, R. (2015). Evidence of collaboration, pooling of resources, learning and role blurring in interprofessional healthcare teams: A realist synthesis. Journal ofInterprofessional Care, 29(1), 20�25. doi:10.3109/13561820.2014.939745

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Spitzer-Shohat, S., Shadmi, E., Goldfracht, M., Kay, C., Hoshen, M., & Balicer, R. D. (2017). Reducing inequity in primary care clinics treat- ing low socioeconomic Jewish and Arab populations in Israel. Journal ofPublic Health (United Kingdom), 39, 2. doi:10.1093/pubmed/fdw037

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https://doi.org/10.3109/13561820.2014.939745

https://doi.org/10.2307/3069458

https://doi.org/10.1093/pubmed/fdw037

https://doi.org/10.1016/j.socscimed.2007.11.021

https://doi.org/10.1525/ae.1997.24.1.219

https://doi.org/10.1186/1748-5908-6-66

https://doi.org/10.1186/1748-5908-6-97

https://doi.org/10.1037/0021-9010.93.4.744

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Abstract
Background
Methods

Setting and design
Data collection and measures
Qualitative phase
Quantitative measures
Analysis

Results

Clinics with a low perceived TE score
Clinics with a high perceived TE score
Clinics with a medium perceived TE score
Kendall’s tau analysis between network measures and perceived TE

Discussion
Conclusion
Funding
References
Primary care networks and team effectiveness: the case of a large-scale quality improvement disparity reduction program