Visualizing faculty development impact: A&amp;nbsp;social network analysis

Foo, Yang Yann; Moody, James; Cook, Sandy

doi:10.1007/s40037-019-0510-9

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Future research will include evaluation of the training program and following the careers of AGE-WELL trainees who complete the program, using such methods as social network analysis (Dawson & Hubball, 2014;Foo et al, 2019) to observe the paths they trace within their own discipline and how those intersect with colleagues across disciplinary divides. Such efforts will afford ongoing monitoring of longer-term, public-facing impacts of former AGE-WELL trainees as they help to address the wicked problem of aging gracefully and well.…”

Section: Discussionmentioning

confidence: 99%

Transdisciplinary training: what does it take to address today’s “wicked problems”?

et al. 2021

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There is a growing need to address today’s “wicked problems” seen in issues such as social justice, global climate crisis and endemic health concerns. Wicked problems are those for which there is no single, clear or optimal solution and thus are amenable to transdisciplinary solutions. Working in a transdisciplinary paradigm is thus seen as an increasingly necessary learned skill, and yet there is a dearth of knowledge on how curriculum centred around transdisciplinarity is perceived by those impacted by such curricula. This study examines the attitudes and responses of Aging Gracefully across Environments using Technology to Support Wellness, Engagement and Long Life NCE Inc.’s (AGE-WELL) stakeholders to the concept and role of transdisciplinarity in a training program intended to equip trainees and research staff from a variety of fields to address the “wicked problem” of aging well in Canada. We conducted 15 in-depth interviews with current AGE-WELL members, trainees as well as researchers and mentors, on the subject of designing the best possible training program. Our data illustrate the complexity of curriculum design and implementation to train for transdisciplinarity. We consider ways in which a shift in culture or ethos in academia may be required to pursue a thoroughly transdisciplinary approach to problem-solving. Short of instituting such a radical culture change as transdisciplinarity, however, strategic and conscientious efforts to integrate multiple and diverse perspectives, to attend carefully to communication and to foreground relationship building may well achieve some of the same goals.

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Section: Discussionmentioning

confidence: 99%

Transdisciplinary training: what does it take to address today’s “wicked problems”?

et al. 2021

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“…83 Lastly, while we did not include studies exploring nonhuman relations, our search of the literature suggested that SNA can be used to explore other domains of medical education, such as curriculum design and content integration. 84,85 Focusing on the use of SNA for educational purposes in other areas of health professions education (e.g., dentistry; nursing; rehabilitation sciences) or other aspects of medical education (e.g., faculty development [86][87][88] or continuing professional development 89,90 ) would also be beneficial.…”

Section: Social Network and Educational Considerationsmentioning

confidence: 99%

Social Network Analysis in Undergraduate and Postgraduate Medical Education: A Scoping Review

Steinert,

Fontes,

Mortaz-Hejri

et al. 2023

Acad Med

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Purpose Social network analysis (SNA) is a theoretical framework and analytical approach used to study relationships among individuals and groups. While SNA has been employed by many disciplines to understand social structures and dynamics of interpersonal relationships, little is known about its use in medical education. Mapping and synthesizing the scope of SNA in undergraduate and postgraduate medical education can inform educational practice and research. Method This scoping review was based on searches conducted in Medline, Embase, Scopus, and ERIC in December 2020 and updated in March 2022. After removal of duplicates, the search strategy yielded 5,284 records, of which 153 met initial inclusion criteria. Team members conducted full-text reviews, extracted relevant data, and conducted descriptive and thematic analyses to determine how SNA has been used as a theoretical and analytical approach in undergraduate and postgraduate medical education. Results Thirty studies, from 11 countries, were retained. Most studies focused on undergraduate medical students, primarily in online environments, and explored students’ friendships, information sharing, and advice seeking through SNA. Few studies included residents and attending staff. Findings suggested that SNA can be a helpful tool for monitoring students’ interactions in online courses and clinical clerkships. SNA can also be used to examine the impact of social networks on achievement, the influence of social support and informal learning outside the classroom, and the role of homophily in learning. In clinical settings, SNA can help explore team dynamics and knowledge exchange among medical trainees. Conclusions While SNA has been underutilized in undergraduate and postgraduate medical education, findings indicate that SNA can help uncover the structure and impact of social networks in the classroom and the clinical setting. SNA can also be used to help design educational experiences, monitor learning, and evaluate pedagogical interventions. Future directions for SNA research in medical education are described.

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“…Network analysis can provide insight for a project on the problem or system being defined [67] as well as leadership effectiveness [68] and connections between network patterns and outcomes [69]. It can also be used to identify gaps or tipping points [70], identify learning pathways [71], evaluate impact [72] or some combination of these [67]. Analyzing the strength and centrality of network ties also provides information on the level of stakeholder influence or marginalization, and can identify brokers [73].…”

Section: Domain: Relationship Orientedmentioning

confidence: 99%