A feasibility study for a unified, multimodal analysis of online information foraging in health-related topics

Szilvia Zörgő; Gjalt-Jorn Peters; Anna Jeney; David Williamson Shaffer; Andrew R Ruis; Rik Crutzen

doi:10.12688/openreseurope.16119.2

A feasibility study for a unified, multimodal analysis of online information foraging in health-related topics

Open Res Eur. 2024 Mar 6:3:98. doi: 10.12688/openreseurope.16119.2. eCollection 2023.

Authors

Szilvia Zörgő¹, Gjalt-Jorn Peters², Anna Jeney³, David Williamson Shaffer⁴, Andrew R Ruis⁴, Rik Crutzen¹

Affiliations

¹ Care and Public Health Research Institute, Maastricht University, Maastricht, Limburg, 6200 MD, The Netherlands.
² Theory, Methodology & Statistics, Faculty of Psychology, Open University of the Netherlands, Heerlen, PO box 2960, 6401 DL, The Netherlands.
³ Baltic-Black Sea Regional Studies Programme, Ivan Franko National University of Lviv, Lviv, Ukraine.
⁴ Department of Educational Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Abstract

Background: Digital health literacy (DHL) is the ability to find, understand, and appraise online health-related information, as well as apply it to health behavior. It has become a core competence for navigating online information and health service environments. DHL involves solving ill-structured problems, where the problem and its solution are not clearcut and may have no single answer, such as in the process of sensemaking. We employ and expand on information foraging theory to address how experts and novices in information retrieval perform a search task. Our overarching aim is to pinpoint best practices and pitfalls in understanding and appraising health-related information online to develop a digital intervention to increase DHL and critical thinking.

Methods: In this feasibility study, we recruited a total of twenty participants for our expert and novice subsamples. We collected sociodemographic data with a self-developed survey, video data through an observation protocol of a 10-minute search task, as well as audio-video data via a retrospective think-aloud. The three, multimodal data streams were transcribed and aligned. Codes were developed inductively in several iterations, then applied deductively to the entire dataset. Tabularized, coded and segmented qualitative data were used to create various quantitative models, which demonstrate viability for the qualitative and statistical comparison of our two subsamples.

Results: Data were visualized with Epistemic Network Analysis to analyze code co-occurrences in the three aligned data streams, and with Qualitative/Unified Exploration of State Transitions to examine the order in which participants in our two subsamples encountered online content.

Conclusions: This paper describes our methods and planned analyses elaborated with mock figures. Quantifying qualitative data, aligning data streams, and representing all information in a tabularized dataset allows us to group data according to various participant attributes and employ data visualization techniques to pinpoint patterns therein.

Keywords: data visualization; digital health literacy; methodology; multimodal data; open source software; unified methods.

Grants and funding

This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 101028644, as well as from University Fund Limburg/SWOL. The opinions, findings, and conclusions do not reflect the views of the funding agency, cooperating institutions, or other individuals.