1Department of Industrial Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
2Laboratory for Healthcare Systems Optimization, Engineering, and Informatics (HCSE), Department of Industrial Engineering, Tarbiat Modares University (TMU), Tehran, Iran
3Department of Psychology, Tehran University of Medical Sciences (TUMS), Tehran, Iran
Background and Objectives: While social network analysis has left a remarkable practical impact in the healthcare field, the potential implication of this methodology in the primary health domain is poorly researched. Hence, this study aimed to explore the use and usefulness social network analysis in the context of primary health care. Methods: The health volunteers of Imam Ali Health Center in Isfahan city (situated in Central Iran) participated in a plan aimed at helping prevention of depression in 20-45-year-old mothers. Each health volunteer was asked to choose 5 to 20 individuals from the population they covered. Data were collected using a questionnaire in which each health volunteer determined which other volunteers they interacted, and what direction and frequency each interaction represented, during administration of the plan. An interaction was defined as the exchange of information related to the plan between volunteers. A series of network structure variables including degree centrality, betweenness centrality, density degree were calculated. A novel function-oriented network variable, termed activity performance was also introduced and calculated. The activity performance rank of the six lowest-betweenness-centrality-rank individuals were compared with their rank in gatekeeping list for validation of the new networ k variable. Findings: The key members, gatekeepers and week members of the analysed social netwrok were identified. The network was revealed to be relatively homogenous. The average distance between individuals was 2.85 across the whole network and ranged from1.03 to 1.86 within the subnetworks. The individuals’ activity performance ranks were congruent with their betweenness centrality ranks, suggesting the validity of the introduced network variables. Conclusions: Social network analysis can help identify the strength and weaknesses of health-related networks in the primary health context. Elucidation of the network structural characteristics can help improve network interactions, reconciling the network paths, and reinforcing its structure, which in turn can lead to a higher performance of network-based health-related plans. The consistency between activity performance rank and betweenness centrality ranks indicate the validity of the former new variable as a complementary measure be used for a more informative social network analysis.