Multidimensional advice networks in primary health care.

AIM: We aimed to examine advice interactions among family physicians using social network analysis (SNA) by categorizing advice interaction according to the five advice dimensions. BACKGROUND: Inter-individual interactions for information exchange is a powerful tool for the pursuit of solutions to issues. These interactions may involve advice-seeking. METHODS: The whole network approach was adopted and face-to-face research was conducted with 139 family physicians. Data were analysed using social network software, UCINET and visualized using the NETDRAW software. To examine the multidimensional advice networks, the frequency, density, reciprocity (dyad) measures were used. The Quadratic Assignment Procedure was used in UCINET to measure the correlations between the dimensions of advice. The Girvan-Newman algorithm was used to examine clustering in the advice network. FINDINGS: Density values in the advice dimensions were very low. This indicates that the network was sparse, with limited interactions among family physicians in terms of giving and receiving advice. The strength of the ties in the dimensions was realized through validation, solutions, problem reformulation, meta-information, and legitimization, respectively. The results showed that the relationships between the dimensions were moderately, positively and significantly correlated. The advice network exhibited high modularity. Family physicians tended to seek advice from colleagues at the family health centers where they worked. We presented a visual representation of advice networks in primary healthcare settings. Identifying multidimensional advice networks through social network analysis can provide insight into how information is disseminated among family physicians. Our findings could contribute to decision makers in developing solution-oriented processes.