Literature DB >> 27807877

Social network analysis in medical education.

Rachel Isba1, Katherine Woolf2, Robert Hanneman3.   

Abstract

CONTENT: Humans are fundamentally social beings. The social systems within which we live our lives (families, schools, workplaces, professions, friendship groups) have a significant influence on our health, success and well-being. These groups can be characterised as networks and analysed using social network analysis. SOCIAL NETWORK ANALYSIS: Social network analysis is a mainly quantitative method for analysing how relationships between individuals form and affect those individuals, but also how individual relationships build up into wider social structures that influence outcomes at a group level. Recent increases in computational power have increased the accessibility of social network analysis methods for application to medical education research. APPLICATION TO MEDICAL EDUCATION: Social network analysis has been used to explore team-working, social influences on attitudes and behaviours, the influence of social position on individual success, and the relationship between social cohesion and power. This makes social network analysis theories and methods relevant to understanding the social processes underlying academic performance, workplace learning and policy-making and implementation in medical education contexts.
CONCLUSIONS: Social network analysis is underused in medical education, yet it is a method that could yield significant insights that would improve experiences and outcomes for medical trainees and educators, and ultimately for patients.
© 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Entities:  

Mesh:

Year:  2016        PMID: 27807877     DOI: 10.1111/medu.13152

Source DB:  PubMed          Journal:  Med Educ        ISSN: 0308-0110            Impact factor:   6.251


  10 in total

1.  Social Network Theory in Interprofessional Education: Revealing Hidden Power.

Authors:  Laura Nimmon; Anthony R Artino; Lara Varpio
Journal:  J Grad Med Educ       Date:  2019-06

2.  A social network intervention to improve connectivity and burnout among psychiatry residents in an academic institution: a quasi-experimental study.

Authors:  Ardavan Mohammad Aghaei; Vandad Sharifi; Maryam Tabatabaee; Fattaneh Abdi-Masouleh; Reza Yousefi Nooraie
Journal:  BMC Med Educ       Date:  2022-05-13       Impact factor: 3.263

3.  How the study of online collaborative learning can guide teachers and predict students' performance in a medical course.

Authors:  Mohammed Saqr; Uno Fors; Matti Tedre
Journal:  BMC Med Educ       Date:  2018-02-06       Impact factor: 2.463

4.  Investigating the existence of social networks in cheating behaviors in medical students.

Authors:  Jorge Monteiro; Fernanda Silva-Pereira; Milton Severo
Journal:  BMC Med Educ       Date:  2018-08-09       Impact factor: 2.463

5.  Debriefing interaction patterns and learning outcomes in simulation: an observational mixed-methods network study.

Authors:  Sandra Abegglen; Robert Greif; Yves Balmer; Hans Joerg Znoj; Sabine Nabecker
Journal:  Adv Simul (Lond)       Date:  2022-09-06

6.  A case study on breastfeeding education in Lebanon's public medical school: exploring the potential role of social networks in medical education.

Authors:  Sara Moukarzel; Christoforos Mamas; Melissa F Warstadt; Lars Bode; Antoine Farhat; Antoine Abi Abboud; Alan J Daly
Journal:  Med Educ Online       Date:  2018-12

7.  Research topics and trends in medical education by social network analysis.

Authors:  Young A Ji; Se Jin Nam; Hong Gee Kim; Jaeil Lee; Soo-Kyoung Lee
Journal:  BMC Med Educ       Date:  2018-09-24       Impact factor: 2.463

8.  What makes an online problem-based group successful? A learning analytics study using social network analysis.

Authors:  Mohammed Saqr; Jalal Nouri; Henriikka Vartiainen; Jonna Malmberg
Journal:  BMC Med Educ       Date:  2020-03-18       Impact factor: 2.463

9.  Diffusion of knowledge and behaviours among trainee doctors in an acute medical unit and implications for quality improvement work: a mixed methods social network analysis.

Authors:  Paul Sullivan; Ghazal Saatchi; Izaba Younis; Mary Louise Harris
Journal:  BMJ Open       Date:  2019-12-10       Impact factor: 2.692

10.  Relationships between medical students' co-regulatory network characteristics and self-regulated learning: a social network study.

Authors:  Derk Bransen; Marjan J B Govaerts; Dominique M A Sluijsmans; Jeroen Donkers; Piet G C Van den Bossche; Erik W Driessen
Journal:  Perspect Med Educ       Date:  2021-04-30
  10 in total

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