Natural Language Processing to Identify Digital Learning Tools in Postgraduate Family Medicine: Protocol for a Scoping Review (Preprint)

Abstract

<sec> <title>BACKGROUND</title> The COVID-19 pandemic has highlighted the growing need for digital learning tools in postgraduate family medicine training. Family medicine departments must understand and recognize the use and effectiveness of digital tools in order to integrate them into curricula and develop effective learning tools that fill gaps and meet the learning needs of trainees. </sec> <sec> <title>OBJECTIVE</title> This scoping review will aim to explore and organize the breadth of knowledge regarding digital learning tools in family medicine training. </sec> <sec> <title>METHODS</title> This scoping review follows the 6 stages of the methodological framework outlined first by Arksey and O’Malley, then refined by Levac et al, including a search of published academic literature in 6 databases (MEDLINE, ERIC, Education Source, Embase, Scopus, and Web of Science) and gray literature. Following title and abstract and full text screening, characteristics and main findings of the included studies and resources will be tabulated and summarized. Thematic analysis and natural language processing (NLP) will be conducted in parallel using a 9-step approach to identify common themes and synthesize the literature. Additionally, NLP will be employed for bibliometric and scientometric analysis of the identified literature. </sec> <sec> <title>RESULTS</title> The search strategy has been developed and launched. As of October 2021, we have completed stages 1, 2, and 3 of the scoping review. We identified 132 studies for inclusion through the academic literature search and 127 relevant studies in the gray literature search. Further refinement of the eligibility criteria and data extraction has been ongoing since September 2021. </sec> <sec> <title>CONCLUSIONS</title> In this scoping review, we will identify and consolidate information and evidence related to the use and effectiveness of existing digital learning tools in postgraduate family medicine training. Our findings will improve the understanding of the current landscape of digital learning tools, which will be of great value to educators and trainees interested in using existing tools, innovators looking to design digital learning tools that meet current needs, and researchers involved in the study of digital tools. </sec> <sec> <title>CLINICALTRIAL</title> Open Science Framework osf.io/wju4k 10.17605/OSF.IO/WJU4K </sec> <sec> <title>INTERNATIONAL REGISTERED REPORT</title> DERR1-10.2196/34575 </sec>

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