Among the 4338 clinical nurses, the mean (SD) age was 34.03 ± 7.30 years, and the mean (SD) working years was 11.95 ± 7.79 years. 94.40% were female, and 5.60% were male. 47.07% were nurses, 47.97% were nurse-in-charge, and 4.96% were associate professor of nursing or above. Other general demographic data are shown in Table 1. Moreover, the mean scores, standard deviations and abbreviations for each item are presented in Table 2.
3.2.1 The characteristics of edges
The network model is shown in Fig. 1. There was total 250 edges (range from − 0.141 to 0.407) in the whole network and 166 edges (range from − 0.141 to 0.210) across the 4 communities in the network.
In the edges across communities, M1 was correlated with 13 nodes of other communities, namely, D1, D2, D4, D5, D8, K1, K3, K4, K6, K7 and P1-3, among which the correlation with K7 was stronger (edge weight: 0.06). M2 was positively correlated with 8 nodes of other communities, namely, D6, D8, K2, K3, K5-7 and P4, among which the correlation with K2 was the strongest (edge weight: 0.08). M3 was positively correlated with 10s node of other communities, namely, D1, D2, D4, D5, D8, K5-7, P1 and P4, among which the correlation with P4 was the strongest (edge weight: 0.11). M4 was correlated with 15 nodes of other communities, namely, D1, D3, D5-8, K1, K4-7, P1, and P3-5, among which the correlation with K1 was the strongest (edge weight: 0.15). M5 was correlated with 12 nodes, namely, D1-6, K1-4, P4, and P5 among which the correlation with K2 was the strongest (edge weight: 0.02). M6 was correlated with 4 nodes, namely, K2, K5, K7 and P1, among which the correlation with K5 was the strongest (edge weight: 0.07). M7 was correlated with 7 nodes, namely, D5, K3-5, K7, P1 and P5, among which the correlation with K4 was the strongest (edge weight: 0.05). M8 was correlated with 13 nodes, namely, D1, D2, D8, K1-7, P1, P2 and P4, among which the correlation with P4 was the strongest (edge weight: 0.08). M9 was correlated with 10 nodes, namely, D1, D2, D5, D8, K3, K5, K7, P1, P4 and P5, among which the correlation with P5 was stronger (edge weight: 0.08). D1 was correlated with 6 nodes, K1, K6, K7 and P3-5, among which the correlation with P4 was stronger (edge weight: -0.07). D2 was correlated with 7 nodes, namely, K1-3, K6, K7, P1 and P4, among which the correlation with K1 was the strongest (edge weight: 0.03). D3 was correlated with 7 nodes, namely, K5-7 and P1-4, among which the correlation with P3 was the strongest (edge weight: 0.11). D4 was correlated with 2 nodes, namely, K2 and K3, among which the correlation with K3 was stronger (edge weight: 0.005). D5 was correlated with 5 nodes, namely, K1, K4, K7, P4 and P5, among which the correlation with P5 was the strongest (edge weight: 0.07). D6 was correlated with 5 nodes, namely, K1, K4, P1, P2 and P5, among which the correlation with P2 was the strongest (edge weight: 0.07). D7 was correlated with 6 nodes, namely, K1, K6, P1, P2, P4 and P5, among which the correlation with P4 was the strongest (edge weight: 0.06). D8 was correlated with 9 nodes, namely, K1-5, K7, P2, P4 and P5, among which the correlation with P5 was the strongest (edge weight: 0.09). K1 was correlated with 5 nodes, namely, P1-5, among which the correlation with P4 was the strongest (edge weight: -0.14). K2 was correlated with 2 nodes, namely, P1 and P5, among which the correlation with P5 was the strongest (edge weight: 0.03). K3 was correlated with 3 nodes, namely, P1, P4 and P5, among which the correlation with P1 was the strongest (edge weight: -0.02). K4 was correlated with 3 nodes, namely, P2, P3 and P5, among which the correlation with P2 was the strongest (edge weight: 0.01). K5 was correlated with 3 nodes, namely, P1, P2 and P4, among which the correlation with P4 was the strongest (edge weight: 0.06). K6 was correlated with 5 nodes, namely, P1-5, among which the correlation with P4 was the strongest (edge weight: 0.21). K7 was correlated with 4 nodes, namely, P1-3 and P5, among which the correlation with P1 was the strongest (edge weight: -0.08). See Supplementary Table 1 of the supplemental material for more detailed information on the correlations among nodes in the network. For edges in biosafety event monitoring and warning abilities community, there were 32 edges ranging from < 0.01 to 0.40, and the strongest correlation was between M6 and M7. For edges in the biosafety incident nursing disposal abilities community, there were 24 edges ranging from < 0.01 to 0.41, and the strongest correlation was between D6 and D7. For edges in biosafety knowledge preparedness community, there were 18 edges ranging from < 0.01 to 0.39, and the strongest correlation was between K2 and K3. For edges in biosafety infection protection abilities community, there were 10 edges ranging from < 0.01 to 0.37, and the strongest correlation was between P1 and P2.
In the biosafety incident response competence network, the 95% confidence interval of edge weights was narrow, which indicated that the accuracy of edge weights was acceptable (see Supplementary Fig. 1 in supplementary material). As the result of the difference significance test of the edge weight showed, the edge weight between K6 and P4 was the largest among the cross-community edges (see Supplementary Fig. 2 in supplementary material).
3.2.2 The characteristics of nodes
The node expected influence is shown in Fig. 2(a). P4, M3, D5 and K5 have the highest expected influence, indicating that these variables are the most associated nodes in the present network from the perspective of statistics. M2 has the lowest expected influence, indicating that this variable is the least associated node in the present network from the perspective of statistics. As shown in Supplementary Fig. 3 of the supplementary material, with the reduction of the subsample, the average correlation of the BEI indices of the original sample and the subsample decreased, while the CS coefficient was 0.517, which was larger than 0.25 and indicated acceptable stability. As shown in Supplementary Fig. 4 of the supplementary material, the BEIs of D8 and P5 were statistically larger than those of at least 88% of the other nodes (P < 0.05).
The node bridge expected influence is shown in Fig. 2(b). In the community of biosafety infection protection abilities, P4 has the highest bridge expected influence, which indicates it has the strongest connections with the other 3 communities. In the community of biosafety event monitoring and warning abilities, M4 has the highest bridge expected influence, which indicates it has the strongest connections with the other 3 communities. In the community of biosafety knowledge preparedness, K1 has the highest bridge expected influence, which indicates it has the strongest connections with the other 3 communities. D8 has the highest bridge expected influence, which indicates it has the strongest connections with the other 3 communities.