scholarly journals Social Medical Capital: How Patients and Caregivers Can Benefit From Online Social Interactions

10.2196/16337 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e16337 ◽  
Author(s):  
Pietro Panzarasa ◽  
Christopher J Griffiths ◽  
Nishanth Sastry ◽  
Anna De Simoni
Keyword(s):  
Social Interactions ◽  
Large Scale ◽  
Big Data Analytics ◽  
Online Health Communities ◽  
Policy Interventions ◽  
Large Scale Network ◽  
Scale Network ◽  
Effective Policy ◽  
Health Communities ◽  
Network Studies

The rapid growth of online health communities and the increasing availability of relational data from social media provide invaluable opportunities for using network science and big data analytics to better understand how patients and caregivers can benefit from online conversations. Here, we outline a new network-based theory of social medical capital that will open up new avenues for conducting large-scale network studies of online health communities and devising effective policy interventions aimed at improving patients’ self-care and health.

scholarly journals Social Medical Capital: How Patients and Caregivers Can Benefit From Online Social Interactions (Preprint)

2019 ◽  
Author(s):  
Pietro Panzarasa ◽  
Christopher J Griffiths ◽  
Nishanth Sastry ◽  
Anna De Simoni
Keyword(s):  
Social Interactions ◽  
Large Scale ◽  
Big Data Analytics ◽  
Online Health Communities ◽  
Policy Interventions ◽  
Large Scale Network ◽  
Scale Network ◽  
Effective Policy ◽  
Health Communities ◽  
Network Studies

UNSTRUCTURED The rapid growth of online health communities and the increasing availability of relational data from social media provide invaluable opportunities for using network science and big data analytics to better understand how patients and caregivers can benefit from online conversations. Here, we outline a new network-based theory of social medical capital that will open up new avenues for conducting large-scale network studies of online health communities and devising effective policy interventions aimed at improving patients’ self-care and health.

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network

Attention Biases Visual Activity in Visual Short-term Memory

2014 ◽  
Vol 26 (7) ◽  
pp. 1377-1389 ◽  
Author(s):  
Bo-Cheng Kuo ◽  
Mark G. Stokes ◽  
Alexandra M. Murray ◽  
Anna Christina Nobre
Keyword(s):  
Large Scale ◽  
Short Term Memory ◽  
Control Mechanisms ◽  
Top Down ◽  
Short Term ◽  
Visual Activity ◽  
Visual Short Term Memory ◽  
Large Scale Network ◽  
Visual Areas ◽  
Scale Network

In the current study, we tested whether representations in visual STM (VSTM) can be biased via top–down attentional modulation of visual activity in retinotopically specific locations. We manipulated attention using retrospective cues presented during the retention interval of a VSTM task. Retrospective cues triggered activity in a large-scale network implicated in attentional control and led to retinotopically specific modulation of activity in early visual areas V1–V4. Importantly, shifts of attention during VSTM maintenance were associated with changes in functional connectivity between pFC and retinotopic regions within V4. Our findings provide new insights into top–down control mechanisms that modulate VSTM representations for flexible and goal-directed maintenance of the most relevant memoranda.

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