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 |
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] |
To explore the influence of homophily on tie formation | |||||
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 | |
To explore the relationship between attitudes toward EIP and network position | Core/periphery model; structural holes | ||||
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 |
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 |