Head-to-Head Comparison of Social Network Assessments in Stroke Survivors

You don't say when these surveys take place, so useless and probably wrong. Properly done would be about three months after leaving the hospital, that will give you enough time to find out you lost most of the people you considered friends.  

So your doctor needs to get you 100% recovered immediately before you lose the first two groups of friends that Aristotle describes.  DEMAND results or your doctor will use the craptastic saying; 'All strokes are different, all stroke recoveries are different'. You can't allow your doctor to hide and cower behind that useless saying.

Aristotle believes that there are three different kinds of friendship; that of utility, friendship of pleasure, and virtuous friendship. 

and that you will likely lose all of the first two post stroke?

Head-to-Head Comparison of Social Network Assessments in Stroke Survivors

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Morgan Prust, MD, Abby Halm, BA, Simona Nedelcu, MD, PhD, ...

First Published August 13, 2020 Research Article 

https://doi.org/10.1177/1941874420945889

Article information

Abstract

Background and Purpose:

Social networks influence human health and disease through direct biological and indirect psychosocial mechanisms. They have particular importance in neurologic disease because of support, information, and healthy behavior adoption that circulate in networks. Investigations into social networks as determinants of disease risk and health outcomes have historically relied on summary indices of social support, such as the Lubben Social Network Scale–Revised (LSNS-R) or the Stroke Social Network Scale (SSNS). We compared these 2 survey tools to personal network (PERSNET) mapping tool, a novel social network survey that facilitates detailed mapping of social network structure, extraction of quantitative network structural parameters, and characterization of the demographic and health parameters of each network member.

Methods:

In a cohort of inpatient and outpatient stroke survivors, we administered LSNS-R, SSNS, and PERSNET in a randomized order to each patient. We used logistic regression to generate correlation matrices between LSNS-R scores, SSNS scores, and PERSNET’s network structure (eg, size and density) and composition metrics (eg, percent kin in network). We also examined the relationship between LSNS-R-derived risk of social isolation with PERSNET-derived network size.

Results:

We analyzed survey responses for 67 participants and found a significant correlation between LSNS-R, SSNS, and PERSNET-derived indices of network structure. We found no correlation between LSNS-R, SSNS, and PERSNET-derived metrics of network composition. Personal network mapping tool structural and compositional variables were also internally correlated. Social isolation defined by LSNS-R corresponded to a network size of <5.

Conclusions:

Personal network mapping tool is a valid index of social network structure, with a significant correlation to validated indices of perceived social support. Personal network mapping tool also captures a novel range of health behavioral data that have not been well characterized by previous network surveys. Therefore, PERSNET offers a comprehensive social network assessment with visualization capabilities that quantifies the social environment in a valid and unique manner.

Keywords stroke, cerebrovascular disorders, social networks, social determinants of health, stroke recovery, network mapping

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