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Sokratis Nifakos

Researcher at Karolinska Institutet

Publications -  19
Citations -  272

Sokratis Nifakos is an academic researcher from Karolinska Institutet. The author has contributed to research in topics: Computer science & Augmented reality. The author has an hindex of 5, co-authored 15 publications receiving 136 citations.

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Mobile Digital Education for Health Professions: Systematic Review and Meta-Analysis by the Digital Health Education Collaboration.

TL;DR: The evidence base suggests that mLearning is as effective as traditional learning or possibly more so, and provides encouraging early evidence to strengthen efforts aimed at expanding health professions education using mobile devices in order to help tackle the global shortage of health professionals.
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Influence of Human Factors on Cyber Security within Healthcare Organisations: A Systematic Review.

TL;DR: A systematic review of the literature on the evolving nature of cybersecurity threats stemming from exploiting IT infrastructures to more advanced attacks launched with the intent of exploiting human vulnerability addresses the complexity of cybersecurity measures adopted within the healthcare and clinical environments.
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Videos as learning objects in MOOCs: A study of specialist and non‐specialist participants' video activity in MOOCs.

TL;DR: The findings indicate that while age and educational background impacts the level of video activity, there is no significant difference between specialists and non‐specialists, and it is concluded that the MOOC format may be suited to non-specialist groups, allowing them to self‐direct their learning and utilise videos as educational resources.
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Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching.

TL;DR: The developed mechanism creates new opportunities in conquering the field of healthcare interoperability, however, according to the mechanism's evaluation results, it is almost impossible to create syntactic or semantic patterns for understanding the nature of a healthcare dataset.