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Digital learning tools for postgraduate family medicine training: Protocol for a scoping review using natural language processing
Hui (Harriet) Yan;
Arya Rahgozar;
Claire Sethuram;
Sathya Karunananathan;
Douglas Archibald;
Lindsay Bradley;
Ramtin Hakimjavadi;
Mary Helmer-Smith;
Kheira Jolin-Dahel;
Tess McCutcheon;
Jeffrey Puncher;
Parisa Rezaiefar;
Lina Shoppoff;
Clare Liddy
ABSTRACT
Background:
Introduction
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.
Objective:
This scoping review will aim to explore and organize the breadth of knowledge regarding digital learning tools in family medicine training.
Methods:
This scoping review will follow the methodological framework outlined by Arksey and O’Malley, including a search of published academic literature in six databases (MEDLINE, ERIC, Education Source, Embase, Scopus, and Web of Science) and grey literature. Following title/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 will be conducted to identify common themes and synthesize the literature. Additionally, natural language processing (NLP) will be employed for bibliometric and scientometric analysis of the identified literature.
Results:
The search strategy has been developed and launched. Data extraction is currently underway.
Conclusions:
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, to innovators looking to design digital learning tools that meet current needs, and to researchers involved in the study of digital tools.
Citation
Please cite as:
Yan H(, Rahgozar A, Sethuram C, Karunananathan S, Archibald D, Bradley L, Hakimjavadi R, Helmer-Smith M, Jolin-Dahel K, McCutcheon T, Puncher J, Rezaiefar P, Shoppoff L, Liddy C
Natural Language Processing to Identify Digital Learning Tools in Postgraduate Family Medicine: Protocol for a Scoping Review