Table 5 Social network measures and analysis

1. Glegg et al. (2019)

(1) network properties (n=8): 28 structural properties, with degree centrality, tie characteristics (e.g., homophily, reciprocity), and whole network density being most frequent

(2) network visualizations (n=13)

(3) conventional descriptive statistics (n=2): e.g., frequency counts, proportions.

(1) regression (n=14): ordinary least squares, ordinal logistic regression, multi-level modeling, P2 logistic regression, linear regression

(2) paired t tests or Wilcoxon ranks (n=3)

(3) Chi-square test (n=2)

(4) exponential random graph models (n=3); quadratic assignment procedure

(5) factor analysis: n=1

2. DuGoff et al. (2018)

(1) provider-level: centrality, degree, density

(2) dyad- and triad-level: Assortativity, distance, edge, Jaccard similarity, reciprocity, recurrence, transitivity

(3) patient-level: care density, team size, provider constellation

(1) a range of different statistical approaches from correlation coefficients to multilevel regression modelling examine the association between network characteristics and aspects of health care utilization

(2) Girvan-Newman algorithm (n=6) and studies used the Blondel model (n=2) to identify clusters of providers

(3) Exponential-family Random Graph Models (n=3); Multiple Membership Multiple Classification model(n=1)

3. Brunson et al. (2018)

motifs, neighbourhood, meso-structure, distance effects

regression (n=27)

exponential random graph model (n=6)

rule mining (n=4)

4. Sabot et al. (2017)

clustering coefficient, component count strong, component count weak, density, diffusion, fragmentation, hierarchy, isolates, centrality, simmelian ties, number of triads, and number of cliques, degree, connectivity, inclusion, reach, and centralization, reciprocity, tie strength

(1) correlations (Spearman Rho), Pearson X 2 and Fisher’s exact test, t test, Chi-squared

(2) multiple linear regression, generalized linear mixed models

(3) qualitative analysis: reflexive observation and contextual analysis, axial coding, themes developed using human factors theory

5. Poghosyan et al. (2016)

(1) individual level: centrality, betweenness centrality, degree centrality

(2) team level: centralization, density, hierarchy, cohesion (subgroup property), isolates, clustering, reciprocity

N/A

6. Mitchell et al. (2016)

density, network role, bridging, size and type of tie (i.e., embedded, boundary crossing), density

descriptive analysis using block models, bivariate and multivariate analyses

7. Bae et al. (2015)

(1) actor-level (n=18)

(2) dyad-level (n=7)

(3) network-level (n=23)

(4) organization-level (n=6)

group cohesiveness analysis (n=18), centrality analysis (n=16), regression (n=5), monadic or dyadic or network hypotheses (n=4), structural equivalence analysis (n=2), visual inspection (n=3), block model analysis (n=2), multidimensional scaling, hierarchical clustering, smallest space analysis, social relations model, correlation analysis

8. Benton et al. (2015)

(1) individual-level, the most frequently reported: in-degree and out‐degree

(2) network-level, the most frequently reported: network densities, network centrality

N/A

9.Tasselli et al. (2014)

network density, centrality, and brokerage

N/A

10. Cunningham et al. (2012)

Three levels: actors, the network (or organisation), and inter-network (or inter-organisation)

organisation (n=8)

actors and network(n=17, three looked at the actors and team)

actors, organisation and external network (n=1)

(1) SNA

(2) other analysis: sociometric analysis, content analysis, multiple regression (n=4), T-tests, survival analysis

11.Chambers et al. (2012)

N/A

N/A

12. Dunn et al. (2011)

(1) indicators of the aggregate properties of networks

(2) indicators based on the locations of individuals within networks

social network analysis, qualitative content analysis

13.Braithwaite et al. (2010)

N/A

social science mixed methods