Back

Bibliometric Analysis of COVID-19 in the Context of Migration Health: A Study Protocol

Pernitez-Agan, S.; Bautista, M. A.; Lopez, J.; Sampson, M.; Wickramage, K.

2020-07-11 public and global health
10.1101/2020.07.09.20149401 medRxiv
Show abstract

IntroductionHuman mobility has been pivotal to the spread of COVID-19 through travel and migration. To mitigate the spread, most countries have imposed strict travel restrictions that have severely affected both the wellbeing and livelihoods of many migrant and mobile populations (both internally and internationally), particularly those from impoverished communities, those affected by humanitarian crises, including populations displaced and/or living in camps and camp-like settings. The need to include migrants (both regular and irregular or undocumented) in national strategic response plans for disease prevention and control has been increasingly recognized. Better understanding of the existing scientific evidence in migration health is crucial in designing effective response measures. In this paper, we present a protocol for a bibliometric analysis of scientific publications on COVID-19 and migration health. Expected study findings aim to provide valuable information to support evidence mapping on COVID-19 and migration health, particularly the identification of important research gaps. Methods and analysisUsing Elseviers Scopus abstract and citation database, a comprehensive search strategy will be applied to map scientific publications on COVID-19 and migration health. The current analysis will focus on research published from 1 January 2020 to 4 May 2020. The search query on migration health will largely focus on migration, migrant and human mobility-related terms. Three reviewers will screen publications for eligibility. The extracted bibliographic information will be analysed to determine the dominant research themes, country coverage and migrant groups. Collaboration networks will be analysed using VosViewer, a network analysis software. A deep dive on dominant research themes or migrant health-related topics will be done by creating visualization network maps of keywords from the retrieved publications. Ethics and disseminationThis analysis will draw on publicly available data and does not directly involve human participants; ethics review is not required.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.