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Table 2 Characteristics of included studies

From: How the study of networks informs knowledge translation and implementation: a scoping review

Citation Study purpose Type of network/setting Network size (# participants) Data collection methods Theoretical perspective
Zappa 2011 [29] To describe relationships for knowledge sharing about a new drug Physician network within a group of 338 hospitals 784 physicians Survey Diffusion of innovation
Yousefi-Nooraie 2014 [37] (same study as 29) To assess factors associated with information seeking in public health Information seeking, expertise recognition, and friendship networks within an urban public health department 15 managers and 13 professional consultants (n = 28) Survey Transactive memory theory; social exchange theory
Yousefi-Nooraie 2012 [36] (same study as 30) To identify the structure of intra-organizational knowledge flow for evidence informed practice Information sharing network 170 directors, managers, supervisors, consultants, epidemiologists, practitioners, and administrative support Social influence theory
Tasselli 2015 [22] To describe knowledge transfer between professions, effectiveness of central actors and brokers, and the influence of organizational hierarchy on access to knowledge Knowledge transfer network in a hospital department n = 118
53 physicians and 65 nurses
Survey and interviews Sociology of professions theory; SNA paradigm
Sibbald 2013 [21] To explore patterns of information exchange among colleagues in inter-professional teams Information seeking and sharing networks within six interdisciplinary primary health care teams n = 28 (nurses, physicians, residents, allied health professionals, e.g., nurses, dietician, social worker); two sites: n1 = 19 and n2 = 8. Survey and semi-structured interviews SNA paradigm
Racko 2018 [47] To examine the influence of social position on knowledge exchange over time Knowledge exchange networks within three academic-clinical KT programs three surveys: n1 = 66; n2 = 70; n3 = 42 clinicians and academics Surveys Social capital theory
Quinlan 2013 [42] To explore mechanisms of information sharing across professional boundaries Knowledge contribution to decision-making by members within multidisciplinary primary healthcare teams (two clinical decisions, so two networks for each of four clinical teams) Nurse practitioners (n = 13 or fewer) Online survey Habermas’ theory of communicative power
Paul 2015 [35] (portion of data from study [45]) To test a model examining the role of triadic dependence on reciprocity and homophily Influence network 33 physicians Surveys SNA paradigm
Patient care network 135 physicians
Menchik 2017 [44] To explore the type of knowledge valued by physicians and the influence of hospital prestige on evidence-seeking behavior and perceived esteem by peers Information seeking and clinical case discussion networks, within six hospitals 126 physicians Survey Social influence theory
Mascia 2018 [27] To explore theoretical mechanisms explaining network formation across clinical sectors Advice-seeking networks within two regional health authorities 97 pediatricians Survey Balance theory; structural holes perspective; homophily principle
Mascia 2014 [33] To explore the association between connectedness with colleagues and frequency of evidence use within a physician network Collaboration networks within 5 health authorities 104 pediatricians Survey Diffusion of innovation; social influence theory; social contagion; strength of weak ties [82]
Mascia 2015 [48] (same data as study [30, 32]) To explore the influence of homophily on tie formation     
Mascia 2011a [30] (same data as study [32, 48] To determine the association between attitudes toward EIP and network structure and to identify predictors of collaborative ties EIP advice-sharing networks within 6 hospitals 297 physicians Survey Homophily principle
Mascia 2013 [32] (same data as study [30, 48]) To explore the relationship between attitudes toward EIP and network position     Core/periphery model; structural holes
Mascia 2011b [31] (same study as [30, 32, 48]) To explore the association between network structure and propensity to adopt EIP   207 physicians   Social contagion; structural holes perspective
Long 2014 [41]
(same data as study [40])
To examine the influence of clustering on past, present, and future collaborations within a translational research network Past, present, and intended collaboration networks within a research network 68 researchers and clinicians Online survey SNA paradigm
Long 2013 [40] (same data as study [41]) To identify key players within a research network, their common attributes, and their perceived influence, power, and connectedness Research collaboration and dissemination networks within a research network
Keating 2007 [45] To describe the network of influential discussions among physicians and to predict network position Frequency of influential conversations relevant to practice within primary care 38 physicians Survey SNA paradigm
Heijmans 2017 [26] To explore relationships between network properties and quality of care Information exchange networks within 31 general practices 180 health professionals (physicians, residents, nurses, pharmacy assistants, social workers) Survey document review (i.e., intervention and referral charting) SNA paradigm
Guldbrandsson 2012 [38] To identify potential national opinion leaders in child health promotion Discussion network within national child health promotion context 153 researchers, public health officials, pediatricians and other individuals Emailed survey item Diffusion of innovation
Friedkin 2010 [25] To examine the association between discussion networks, marketing, and physician prescribing practices Advice and discussion networks of physicians (re-analysis of Coleman, Katz and Menzel, 1966 historical data on medication adoption) 125 physicians Document review (i.e., prescription records of pharmacies) Diffusion of innovation; social contagion; cohesion; structural equivalence
Burt 1987 [24] To test social contagion theory by examining cohesion versus structural equivalence as drivers of tie formation
Doumit 2014 [34] To identify opinion leaders and their impact on EIP Advice networks of craniofacial surgeons within 14 countries 59 craniofacial surgeons Online survey Diffusion of innovation
Di Vincenzo 2017 [28] To explain the impact of research productivity on tie redundancy (i.e., connections that lead to the same people/information) Advice seeking networks within and external to a health authority containing 6 hospitals 228 physicians Survey Structural holes perspective; homophily perspective
Bunger 2016 [43] To evaluate change in advice ego-network composition and its impact on whole network structure following implementation of a “learning collaborative” model in improve care quality Advice networks of clinicians (psychologists, social workers, others) and leadership in 32 behavioral health agencies 132 clinicians, supervisors and senior leaders Surveys SNA paradigm
Ankem 2003 [23] To understand communication flow and its influence on awareness/adoption of a treatment, and to identify opinion leaders with influence Frequent discussion networks within a sample drawn from an online physician directory 32 interventional radiologists Phone interviews Diffusion of innovation; SNA paradigm
D’Andreta 2013 [39] To compare the network structures of three research/KT program initiatives Informal advice giving and seeking networks within each of three academic-clinical KT programs n = ~ 260 (directors, managers, program leaders, knowledge brokers, researchers, and others unspecified) Online survey SNA paradigm; Epistemic differences perspective
  1. SNA social network analysis, EIP evidence informed practice, KT knowledge translation. Where indicated by the articles’ authors, the dependent variable is designated using bold text