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Table 3 Variables, network properties and key findings

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

Citation

Primary data analysis method

Variables of interest

Findings

Attributes

Structural or relational parameters

Network property used as proxy for structural parameter

Descriptive/exploratory studies

 Yousefi-Nooraie 2012 [36] (same study as [37])

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

 Long 2014 [41] (same study as [40])

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

 Long 2013 [40] (same study as [41])

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

 Yousefi-Nooraie, 2014 [37] (same study as [36])

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

 Mascia 2015 [48] (same data as [31], [30] and [32])

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

 Mascia 2013 [32] (same data as [48], [30] and [31])

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., importancerelevant 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)

 Mascia 2011b [31] (same data as [48], [32] and [30])

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)*

 Mascia 2011a [30] (same data as [32, 48])

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

  1. KT knowledge translation, EIP evidence informed practice. Where indicated by the article’s author, italic text = dependent variable; * = covariate