Wikipedia: an unexplored resource for understanding consumer health information behaviour in library and information science scholarship

Purpose

To date, health information behaviour (HIB) models have not been applied to an exploration of Wikipedia as a consumer health information resource. Wikipedia has been situated and is well established as a valuable resource for the general layperson wishing to learn more about their health or the health of a loved one. This paper aims to identify an approach to exploring the role of Wikipedia in consumer health information behaviour (CHIB) that is grounded in a conceptual framework from the library and information science (LIS) discipline.

Design/methodology/approach

The author draws on current HIB models and relevant theories from existing LIS literature and applies them to propose a new definition of CHIB. The author uses this definition to frame Wikipedia as an unexplored consumer health information resource in the LIS scholarship and suggests future directions for placing such investigations within a conceptual framework from LIS.

Findings

The paper finds that Longo's expanded conceptual model of health information-seeking behaviour (ECMHISB) could be valuable and useful for the exploration of CHIB in relation to Wikipedia's health and medical content. Due to Wikipedia's online nature, research framed by these models must acknowledge and take under consideration the digital divide phenomenon and various factors that influence an individual's place within it.

Research limitations/implications

This work builds a foundation upon which future research into the role of Wikipedia's health and medical content in CHIB can be grounded. Using Longo's model, future research might provide insight into who Wikipedia is helping and who it has left behind. LIS scholars, practicing health librarians and perhaps health workers stand to gain a deeper understanding of the potential influence of Wikipedia's health information on its consumers.

Originality/value

For LIS scholars, this paper is novel in the fact that a HIB model has not yet been applied to the study of Wikipedia's health content. This paper provides a foundation for this research.

COVID-19 research in Wikipedia

Wikipedia is one of the main sources of free knowledge on the Web. During the first few months of the pandemic, over 5,200 new Wikipedia pages on COVID-19 were created, accumulating over 400 million page views by mid-June 2020. 1 At the same time, an unprecedented amount of scientific articles on COVID-19 and the ongoing pandemic have been published online. Wikipedia’s content is based on reliable sources, such as scientific literature. Given its public function, it is crucial for Wikipedia to rely on representative and reliable scientific results, especially in a time of crisis. We assess the coverage of COVID-19-related research in Wikipedia via citations to a corpus of over 160,000 articles. We find that Wikipedia editors are integrating new research at a fast pace, and have cited close to 2% of the COVID-19 literature under consideration. While doing so, they are able to provide a representative coverage of COVID-19-related research. We show that all the main topics discussed in this literature are proportionally represented from Wikipedia, after accounting for article-level effects. We further use regression analyses to model citations from Wikipedia and show that Wikipedia editors on average rely on literature that is highly cited, widely shared on social media, and peer-reviewed.