Exploring Medical Curricula Using Social Network Analysis Methods

Víta, Martin; Komenda, Martin; Pokorná, Andrea

doi:10.15439/2015f312

Cited by 2 publications

(5 citation statements)

References 11 publications

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“…The obtained results were verified by representatives of the faculty management, in order to confirm the final interpretation [3]. Based on the assessment of the graphical presentation of the subject Psychiatry we decided that the best threshold for cosine similarity is 0.6 (see Fig.…”

Section: E Evaluation and Deploymentmentioning

confidence: 74%

“…The research methods stems from our previously published and reviewed papers [3]- [5], wherefrom the well-known and proven step-by-step data mining guide CRISP-DM (CRossIndustry Standard Process for Data Mining) [6]. We have used the CRISP-DM reference model, which was primarily developed by means of the effort of a consortium from the business sphere, as a powerful scientific technique to excavate the knowledge and patterns concealed in the diversely complex mountain of medical and healthcare education data [7].…”

Section: Methodsmentioning

confidence: 99%

“…The concept of selected network/graph centrality measures was firstly developed in social network analysis. We successfully used these methods in our previous work [3], [4], namely we deal with the closeness, betweenness, and eigenvector centralities.…”

Section: Modelingmentioning

confidence: 99%

“…Input data for the algorithm are represented by trained word2vec model, given document (bag of words for individual medical disciplines), word similarity threshold t, number of keyword n. Finally, we have implemented a new online dynamic visualization as a feature of the OPTIMED reporting tools 3 , which is based on outputs from described algorithm above. Innovative graphical interpretation allows users to create complex data overview, which cannot be achieved with basic data views.…”

Section: Modelingmentioning

confidence: 99%

See 3 more Smart Citations

Automatic Keyword Extraction from Medical and Healthcare Curriculum

Komenda

Karolyi

Pokorná

et al. 2016

Annals of Computer Science and Information Systems

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Abstract-Medical and healthcare study programmes are quite complicated in terms of branched structure and heterogeneous content. In logical sequence a lot of requirements and demands placed on students appear there. This paper focuses on an innovative way how to discover and understand complex curricula using modern information and communication technologies. We introduce an algorithm for curriculum metadata automatic processing --automatic keyword extraction based on unsupervised approaches, and we demonstrate a real application during a process of innovation and optimization of medical education. The outputs of our pilot analysis represent systematic description of medical curriculum by three different approaches (centrality measures) used for relevant keywords extraction. Further evaluation by senior curriculum designers and guarantors is required to obtain an objective benchmark.

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Section: E Evaluation and Deploymentmentioning

confidence: 74%

Section: Methodsmentioning

confidence: 99%

Section: Modelingmentioning

confidence: 99%

Section: Modelingmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Keyword Extraction from Medical and Healthcare Curriculum

Komenda

Karolyi

Pokorná

et al. 2016

Annals of Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…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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Exploring Medical Curricula Using Social Network Analysis Methods

Cited by 2 publications

References 11 publications

Automatic Keyword Extraction from Medical and Healthcare Curriculum

Automatic Keyword Extraction from Medical and Healthcare Curriculum

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

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