Contributions to the literature
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This review synthesizes the KT literature employing a social network analysis (SNA) approach to the study health professionals, to demonstrate the utility of SNA for advancing KT science
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Also summarized is the use of theory in this SNA research to demonstrate the fit of SNA with theoretical approaches used in KT research, including diffusion of innovation and complexity theory
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This article acts as a reference tool for those considering applying a SNA lens to their KT research, to support the design of SNA-specific research questions, methodological approaches, and measures
Background
SNA term (frequency count) | Definition | Implication for KT |
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Network | An interconnected group of actors (e.g., people, organizations) [7] | Provides the social context within which KT occurs |
Actor | A point (node) in a network that represents an individual, organization or entity connected to other actors (through ties) [7] | Represents the people, teams, or organizations involved in KT processes |
Tie (2) | The relations or connections among actors in the network [79] | Represents the interactions, collaborations, or relationships involved in KT Measures one- versus two-way communication, advice seeking, collaboration, etc. [7] |
Dyad | Pairwise relations between actors [7] | Represents one of three levels of analysis for social network data (the others being individual node-level and whole network level) [7] |
Centralization | ||
Whole network centralization (3) | ||
Centrality | ||
Degree centrality (3) | # of direct ties (connections) of an actor | |
Indegree centrality (10) | # of individuals who send (identify) ties to an actor | Considered an index of importance [28] power or influence [40] |
Outdegree centrality (5) | # of direct ties an actor sends (identifies) to others [33] | Used to quantify access to resources through colleagues, exposure to evidence and others’ practices; positively associated with EIP use [33] |
Betweenness centrality (4) | Extent to which an individual is tied/connected to others who are not connected themselves [40] | |
Flow betweenness centrality (3) | How involved an actor is in all of the paths or routes between all other actors (not just those representing the shortest paths) [79] | |
Closeness centrality (2) | Proportion of actors that can be reached in one or more steps [79] | |
Bonacich centrality (1) | Extent to which an actor is tied to others, weighted according to the centrality (e.g., popularity, importance) of those to whom the actor is tied/connected [79] | Proxy for power or hierarchy within a network; may help to identify network fragmentation/brokering opportunities [14] |
Hubs and authorities centrality (1) | The structural prominence of individuals within a core-periphery structured network [32] | Proxy for importance [32] |
Tie characteristics | ||
Tie strength (7) | Value associated with a tie/connection, e.g., frequency of contact, emotional intensity, duration of connection, etc. [7] | Weak ties thought to increase access to new information/opportunities; strong ties seen as required for innovation implementation [82] |
Tie homophily (includes external-internal or EI index) (13) | Similarity of connected actors/nodes on a given attribute [7] | |
Tie hierarchy (1) | Connections between actors dissimilar in their status (e.g., according to profession, leadership or power position) [7] | Hierarchy may be a barrier to innovation adoption (e.g., lack of interest from above/resistance from below [29] |
Tie reciprocity (8) | The extent to which directional ties to actors are reciprocated (i.e., are bi-directional) [79] | Reciprocity may reflect greater stability or equality (versus hierarchy) [79] |
Euclidian distance (1) | A measure of the dissimilarity between the tie patterns of each pair of actors in the network [79] | Can be used to identify key people by their dissimilarity to others (e.g., who has the most research productivity relative to their connected peers) [28] (as a proxy of influence) |
Density | ||
Whole network density (8) | An index of the proportion of existing ties relative to all possible ties in a network [79] | |
Ego network density (2) | ||
Subgroups | ||
Components/isolates (3) | Portions of the network that contain actors connected to one another, but disconnected from actors of other subgroups [79] | Subgroups and isolates can be targeted to increase connectedness, share information, or mobilize action |
Cliques (1) | Maximum # of actors who share all possible connections among themselves [79] | Can describe paths for fostering awareness and adoption of interventions [23] |
Clusters (4) | Dense sets of connections in a network [79] | |
Network roles and positions | ||
Brokers (1) | Actors holding bridging positions in a network (i.e., play a role in connecting subgroups) [79] | |
Coreness/Core-periphery index (2) | The core of a network represents the maximally dense area of connections, whereas the periphery represents (to the maximum extent possible), the set of nodes without connections within their group [79] | |
Structural equivalence (2) | When two actors/nodes have the same relationships to all other nodes in the network—they can be substituted without altering the network [79] | |
Structural holes/constraint (ego network) (2) | Structural holes: absent ties in a network that limit exchange between actors; constraint: degree to which an actor is tied to others who are themselves connected [79] | |
Transitivity/network closure (i.e., network structure related to triads) | ||
Alternating k-stars (4) | The tendency of actors to create ties [29] | |
Alternating k-triangles/transitive triads and/or non-closure structures (5) | Assesses tendency to build relationships outside of one’s local group—access to new knowledge [29] | |
Cyclic closure (1) | The tendency for transitive triads (sets of three actors in which two ties exist) to lead to reciprocal ties within that triad [27] | Cyclic closure thought to reflect non- hierarchical knowledge exchange, which is more effortful to maintain and therefore less likely to be seen in knowledge sharing networks [27] |
Alternating independent two-paths (2) | Assesses the conditions required for transitivity (i.e., ties that form between each pair of actors in a set of three actors) [29] | Can determine the extent to which actors tend to build small, closed, non-hierarchical connections that limit broader access to new information [29] |
Methods
Step 1: identify the research question
Step 2: identify relevant literature
Step 3: select the literature
Step 4: chart the data
Step 5: collate, summarize, and report results
Results
Publication characteristics
Citation | Study purpose | Type of network/setting | Network size (# participants) | Data collection methods | Theoretical perspective |
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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 |
Study design and data collection
Networks and actors
Study purposes and data analysis methods
Describing networks
Testing hypotheses (network modeling)
Citation | Primary data analysis method | Variables of interest | Findings | ||
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Attributes | Structural or relational parameters | Network property used as proxy for structural parameter | |||
Descriptive/exploratory studies | |||||
Deriving network properties to describe the network | _ | Connectedness | Whole network density | Low density (1.2%) observed | |
Information exchange | Tie reciprocity | Head management division identified as central cluster bridging organizational divisions, with hierarchical information flow. Expertise recognition and information seeking clustered within divisions; friendships spanned departments; Friendship and expertise recognition predicted information seeking ties; Network-identified brokers should receive same interventions and supports as formal brokers | |||
Prestige (key actors) | Indegree centrality | ||||
Mediating power of actors | Betweenness centrality | ||||
Subgroups of connected actors | Clusters | ||||
Brokers (actors connecting distinct teams/clusters of alters) | Brokerage patterns (measured by which groups the information source amd its recipients belonged) | ||||
Sibbald 2013 [21] | Deriving network properties to describe the network | – | Cohesiveness related to giving and seeking research-related information | Whole network density | Low density for information seeking and giving (7–12%) observed; suggested these behaviors not a central focus of the interprofessional relationships |
Profession | Key players (prestige) in giving and seeking research information | Indegree centrality | Medical residents prominent in knowledge exchange; physician seen as primary implementer of evidence; nurses as intermediaries between physicians and support staff; allied health more likely to draw information from external networks | ||
Quinlan 2013 [42] | Deriving network properties to describe the network | Profession tenure of the team Occupational distance among members Number of team members | Communicative power (i.e., the facilitation of mutual understanding among other team members) | Flow betweenness centrality | True interprofessional decision-making attributed to low structural hierarchy. Nurse practitioners (in newly formed teams) and registered nurses (in established teams) tended to have greatest communicative power. Mutual understanding and professions’ involvement varied across clinical decision-making episodes |
Change in flow betweenness centrality between clinical decisions | |||||
Descriptive SNA; correlations among specific network properties and/or attributes | Geographic proximity profession (e.g., clinicians/researchers) | Grouping based on similarity in attributes | # components | Geographical proximity, professional homophily associated with clustering (past collaborations); geographical proximity and past collaborative ties influenced current and future collaborations. Intended future collaborations were more interprofessional. Network density varied across networks (current 4%, future 27%, past 31%). Weak ties and reputation associated with intention for future collaboration; strong ties associated with current collaborations. | |
External-internal (E-I) indices based on tie homophily | |||||
Clustering coefficients (comparing ego-and whole network density) | |||||
– | Past strong collaborations; Current or future collaborations | Past collaboration network tie strength; Current or future collaboration ties | |||
Previous experience in the field | Current or future collaborations | Current or future collaboration ties | |||
Actor’s reputation | Indirect contacts | Future collaboration network tie strength | |||
Future intended collaborations | Future collaboration ties | ||||
Chi square analyses to test for association between attributes and network position (i.e., key actor status) | Current workplace Gender Membership in other networks Qualifications Approach to work within the network | Key actors (with respect to power, influence or connectedness) | Indegree centrality Betweenness centrality | A manager, and specific researchers and clinicians identified as key players. Apart from expert status and valuing adequate network resources, network-identified central players and formal brokers had little in common. Planned interventions and support for formal broker roles may be misdirected if not also offered to network-perceived central players or brokers. Network members may be better able to correctly identify central actors than formal brokers | |
Guldbrandsson 2012 [38] | Traditional descriptive statistics, e.g., frequency counts, percentages | Profession Work organization Geographical region | Information seeking about child health promotion | Tie homophily (%) Indegree centrality (frequency of being named) | Organization and professional field were shared in nearly half of all information seeking ties |
Doumit 2014 [34] | Percentage of people nominating an actor; descriptive statistics (frequency counts, percentages) | – | Influence by central actors | Degree centralization | Six individuals with high credibility influenced 85% of the network, suggesting opinion leaders have potential for supporting evidence use |
Reasons for change in medical approach (proportions) | – | – | |||
Barriers to clinical decision-making (proportions) | – | – | |||
D’Andreta 2013 [39] | Deriving network properties to describe the network (descriptive SNA) | KT model adopted | Prestige within the network | Degree centralization | KT teams with different models of KT (i.e., focus on research dissemination vs. knowledge co-production and brokering vs. integrated research-clinical collaboration) varied in their structural properties (e.g. the prominence and control of leaders in KT processes) |
Control over knowledge | Betweenness centralization | ||||
Access to knowledge | Closeness centralization | ||||
Alternate paths for knowledge flow that circumvent central actors | Flow betweenness centralization | ||||
Organizational role (e.g., director, support staff) | Core actors—dominant individuals with frequent knowledge exchange | Coreness scores (core-periphery algorithm) | |||
Predictive/explanatory studies | |||||
Zappa 2011 [29] | Descriptive SNA | External communication (# visits from drug representatives); research orientation (# publications); clinical experience; hierarchical position (administrative role) Medical specialty* Hospital affiliation* | Colleagues with whom knowledge is discussed and transferred | Network density | Low network density (0.3%) |
Components | Multiple small components suggested lack of strong opinion leaders to drive treatment adoption. Findings suggest physicians tend to build small, closed, non-hierarchical internal, and external connections within their professional group, potentially limiting broader access to new information Isolates tended to be clinically experienced and active in research. Advice sharing more likely if physicians shared a medical speciality, geographic proximity but differed in research productivity or years in practice. | ||||
Exponential Random Graph models (p* models) |
Tendency to exchange information with a number of sources
| Alternating k-stars | |||
Tendency to share knowledge within a small peer group (network closure) | Alternating k-triangles; alternating independent two-paths | ||||
Tendency to interact with similar others
Prominence as a knowledge source in the network | Tie homophily/hierarchy; indegree centrality | ||||
Descriptive SNA | Relative connectedness of actors of a given role | Indegree centrality Outdegree centrality | Managers identified as key brokers in KT interventions and EIP implementation processes. Public health professionals preferred to limit advice seeking and expert recognition to a small number of peers; advice seeking limited to own division. Strong friendship ties a significant predictor of information seeking ties. EIP scores not predictive of information seeking or expert recognition ties. | ||
*Role (e.g., manager) *Organizational division Score on EIP implementation scale | Key individuals | Degree centrality | |||
Organizational division | Tendency to connect to peers from other units | E-I index | |||
– | Tendency to reciprocate expert recognition and information seeking ties | Tie reciprocity | |||
Exponential random graph modeling (ERGM) | *Role (e.g., manager) *Organizational division Score on EIP implementation scale | Tendency to connect with those with similar attributes | Tie homophily | ||
Reciprocity | Tie reciprocity | ||||
Formation of information seeking and expertise-recognition ties | Ties and direction of ties (in vs. out) | ||||
Tendency for network to have highly connected nodes (hubs) | Alternating in-k-stars Alternating out-k-stars | ||||
Friendship connections | Ties | ||||
Multilevel logistic regression | *Role (e.g., manager) *Organizational division Score on EIP implementation scale | Tendency to connect with those with similar attributes | Tie homophily | ||
Formation of information seeking and expertise-recognition ties | Ties | ||||
Friendship connections | Ties | ||||
Tasselli, 2015 [22] | Paired t test Linear regression | *Gender *Tenure *Profession *Organizational unit *Rank (i.e., leadership role) |
Ease of knowledge transfer
Perceived receipt of useful knowledge
Connectedness Hierarchy Network fragmentation Individual reach Brokerage potential *Network size |
Tie strength
Mean degree centrality Bonacich centrality Mean betweenness centrality Closeness centrality Betweenness centrality *Degree centrality | Knowledge tends to transfer within rather than across professions; nurses’ networks were denser and more hierarchical; closeness centrality positively associated with ease of knowledge transfer; brokering positions increased access to useful knowledge |
Menchik 2010 [44] | OLS regression | # medical literature database searches per month # journals read regularly *Age *Gender *Tenure at hospital *Medical school *% clinical time *Sub-specialization Prestige of hospital (published rankings) |
Relational esteem by colleagues
| Indegree centrality | Physicians in higher prestige hospitals were less likely to be named as advice givers. Prestige in these settings associated with medical school attended. In lower-prestige hospitals, regularly reading a range of journals, and less time spent on clinical work increased likelihood of high esteem by colleagues |
Mascia 2014 [33] | Ordinal logistic regression | Self-reported frequency of EIP use *Gender *# patients in caseload *area of clinical practice (e.g., asthma, urology, etc.) *# article subscriptions *perceptions of barriers to availability of evidence *perceptions of difficulty applying evidence to practice *Organizational affiliation *Affiliation to formal groups *Collaborative nature of actor’s medical practice | Degree of collaboration with colleagues | Outdegree centrality | Degree centrality directly associated with physicians’ EIP use |
Mascia 2018 [27] | Exponential random graph models | *Past task force involvement *Tenure *Gender *Geographic distance *Association members *Health district | Tendency to reciprocate advice Tendency to seek advice from an indirect tie Tendency for local, generalized exchange of advice *Tendency to form advice ties *Popularity as an advice source *Advice-seeking activity *Brokering | Tie reciprocity Transitivity (path closure) Cyclic closure *Density *Indegree centrality *Outdegree centrality *Formation of non-closure structures *Tie homophily (of attributes) | Advice ties unlikely unless reciprocated; advice ties tended to organize around clusters—driven by transitivity, not popularity; Tendency against exchange of advice in cyclic structures; positive relationship between ties and association homophily in one health authority, and between ties and district/task forces in the other; tendency toward homophily related to tenure and distance, but not gender |
Multiple regression-quadratic assignment procedure (MR-QAP) | Age Gender Tenure/seniority |
Frequency of collaboration
Similarity of professional role, institution and geographical location |
Tie strength
Tie homophily | Ties more likely if specialization, institution were the same between individuals; less likely if similar roles, greater difference in time since graduation and further geographic distance; professional homophily better predictor than institutional homophily | |
Descriptive SNA | Age* Gender* Hospital tenure* Tenure in health authority* Managerial role* Geographical distance from colleagues* Affiliation with other organizations* Self-reported EIP adoption (i.e. frequency of database searching) | Connectedness | Whole network density | Low density (5.7%) observed | |
OLS regression | Network authority (i.e., importance—relevant and popular) | Hubs and authorities centrality | The most active EIP practitioners likely to be found at network periphery (i.e. least central) | ||
Degree of coreness in the network
| Network coreness score (degree centrality and core-periphery position) | ||||
Ordinal logistic regression | Tendency to adopt EIP (self-reported frequency of peer-reviewed research use) Age* Gender* Tenure in health authority/organization* Managerial role* # publications* Perceived access to evidence* Hospital affiliation* | Extent to which a given tie is redundant because of concurrent ties with another alter | Ego-network constraint | Physicians with greater network constraint (i.e., many redundant ties) reported decreased EIP adoption. May be related to information bias—tendency of physicians to interpret the information in a way that is congruent with their previous knowledge or opinion. High degree centrality associated with EIP use. | |
Individual’s network size* | Total # of ties in ego-network (indegree + outdegree centrality)* | ||||
Descriptive SNA | – | Average number of advice exchange colleagues | Mean ego-network density | Advice sharing most likely when physicians shared a medical specialty, geographic proximity, similar attitudes toward EIP, or had co-authored publications. Collaboration less likely when actors held similar managerial roles, or were at different hospitals/clinical/geographical areas. | |
Tendency for colleagues to both give and receive advice with one another | Tie reciprocity | ||||
Multiple regression quadratic assignment procedures (MR-QAP analysis) | – |
Advice exchange among pairs of physicians
| Ego-network ties | ||
Similarity between pairs of tied actors in: Geographical distances Gender* Age* Medical specialization* Clinical experience* Tenure in health authority/organization* Managerial role* # publications* Co-authorship* EIP adoption (self-reported frequency of peer-reviewed research use)* | Tie homophily | ||||
Paul 2015 [35] | Extended p2 model with Bayesian modeling and estimation | *Age Patient age Patient sex Patient race Patient health status Patient intensity of care | Relative # shared patients *Same gender *Same specialty *Same location Reciprocity Social dependence (clustering) | Density Tie homophily Tie reciprocity Alternating independent two-paths Transitive triads Alternating k-stars (two-stars) | Low network density (0.10) observed. Triadic clustering higher than chance. Ties not associated with gender/specialty homophily. Location positively associated with ties. Complementary expertise positively associated with patient sharing. Transitivity may account for reciprocity |
Gender Clinic % female patients Self-identify as expert # clinics per week # years practicing in the city Tenure at hospital Years clinical experience Location of training |
Involvement in influential discussions
| Whole network density | Low density (0.154) observed; reciprocity more likely than not—may be an artifact of transitivity; high triadic clustering observed; same clinic and gender, expert, higher clinical caseload increased tendency for tie formation | ||
Keating 2007 [45] | P2 logistic regression analysis | Self-identified experts seen as more influential; no relationship between # years in practice or location of work or training. Clustering with respect to EIP knowledge exchange observed between those with greater # of patients and higher frequency of clinical sessions. High reciprocity observed in the absence of opinion leaders with high centrality. | |||
Being perceived as influential
| Indegree centrality | ||||
Perceiving others as influential (information seeking) | Outdegree centrality | ||||
Reciprocity
| Tie reciprocity | ||||
Heijmans 2017 [26] | Paired sample t tests/Wilcoxon tests Logistic multi-level analyses | *Patient age *Patient sex *Patient group *Patient illness status *Treatment/control group for parallel randomized controlled trial | Connectedness Frequency of contact Influence of coordinator Similarity in attitudes related to treatment goals Presence of opinion leader *Network size | Density Tie strength Degree centrality Homophily (E-I index) % of possible in-degree ties | Low density (0.37 and 0.38) observed. Most ties between those who did not value achieving treatment goals. General practitioner most likely named as opinion leader. Nurse performance associated with consistently identified opinion leader. Lack of tie homophily for positive attitudes associated with poor clinical outcomes |
Friedkin 2010 [25] | Random intercept multi-level event history model | Professional age Chief or honorary position (yes/no) Number of journals read Value keeping up with scientific developments Physicians’ adoption of a new antibiotic (i.e., prescribing behavior) Marketing patterns of drug companies Proportion of previous adopters at a given time (“internal contagion”) | Influence of advisors/discussion partners | Contact network role (CNET)—a summative measure of 4 measures of structural cohesion and structural equivalence; position in the medical advice network | Cohesion and structural equivalence were correlated, and may be useful in combination to improve reliability in the evaluation of network structures across settings |
Di Vincenzo 2017 [28] | Ordinary Least Squares regression | # publications *Tenure *Managerial role *Geographic distance *Hospital affiliation *# publications from same-specialty colleagues | Dependence on others/access to new information Relative productivity among ego-network colleagues Same role Same specialty | Ego-network constraint Euclidian distance *Ego-network size | Young employees appeared to have more redundant networks (greater need for advice). Hospital affiliation (i.e., context) influenced constraint. Constraint negatively associated with ego-network size and relative productivity (mediated by professional group membership), positively with productivity, Euclidean distance, role/specialty homophily (augments impact of productivity on prestige) |
Burt 1987 [24] | Ordinary least squares regression with likelihood-ratio chi-squared test | Timing of adoption Relative timing of adoption within the network Professional age Contact with drug company Number of journals read Number of house calls vs. office visits Value keeping up with scientific developments | Position in the medical advice/discussion network | Structural equivalence | Adoption by others in equivalent positions within the network was a stronger predictor of adoption than adoption by those in an individual’s advice or discussion networks. Early adopters tended to participate in a range of EIP behaviors. Adoption by prominent physicians seen to be related to their desire to avoid being late adopters. |
Influence of advisors/discussion partners | Structural cohesion | ||||
Ankem 2003 [23] | Chi-square statistics | Preferred information source Timing of awareness of the intervention
Timing of intervention adoption
| – | – | Clinical networks were most prominent in fostering awareness and adoption of a clinical intervention, but research and social networks also likely to influence these processes. Early adopters tended to rely on journals and conferences for information informing practice change; late adopters to a greater extent by network contacts. |
Factor analysis | Specialization Hospital City Timing of adoption Frequency of communication with colleagues | Types of relations within the network (e.g., clinical, research, leisure) | Ties | ||
Groupings of information exchange relations
|
Cliques
| ||||
Longitudinal evaluative studies | |||||
Racko 2018 [47] | Ordinary least squares regression | Professional status (ranking) *Professional role *Gender *Education *Organizational status | Research collaboration Joint decision-making Connectedness to high-status individuals Connectedness to knowledge brokers Connectedness to unfamiliar peers *Intra-professional whole network size | Ego-network size via tie heterophily Tie strength Mean status score of ego-network relative to whole network % of possible ties *Tie homophily | Higher social status associated with more research collaboration at all time points, and joint decision-making in early phases. Higher-status ties with peers, ties to formal knowledge brokers and ties to unfamiliar peers inconsistently predicted knowledge exchange, research collaboration and joint decision-making over time. Formal knowledge broker presence may facilitate interprofessional networking |
Bunger 2016 [43] | Paired t tests; one-way analysis of variance; descriptive SNA | Role (faculty expert, internal colleague, external peer, private practitioner, other) | Connectedness Lack of advice seeking/sharing Reciprocity Similarity in connectedness Tendency for sub-groups to form Same institution Frequency of communication | Density Isolates Tie reciprocity Indegree centralization Clustering Tie homophily Tie strength | Ego-network size decreased, more markedly for senior leaders. Exposure to private practitioners and “others” decreased; exposure to experts increased. Substantial turnover in dyads was reported, with greater tie density around central core of experts. Reciprocity and tie heterophily increased over time |