Analysis of web information-seeking behavior of users with different levels of health literacy

Literacia em Saúde é definida como "o nível pelo qual os indivíduos podem obter, processar, compreender e comunicar informação relacionada com saúde necessária para tomar decisões de saúde informadas". Os utilizadores com um baixo nível de literacia em saúde têm menos conhecimentos das suas condições médicas, maior dificuldade em seguir as instruções e compreender a informação dada pelos médicos. Cada vez mais, as pessoas recorrem à web para pesquisar sobre informação de saúde. As dificuldades que os utilizadores de baixa literacia têm no mundo real provavelmente persistem no mundo virtual. O principal objetivo deste estudo é analisar os comportamentos de pesquisa de utilizadores com diferentes níveis de literacia em saúde. Pretende-se identificar diferenças entre pessoas com baixa e alta literacia de saúde que depois possam ser utilizadas para a melhoria dos sistemas de recuperação e contribuir, entre outros, para facilitar o acesso à informação e educação das pessoas com baixa literacia. Este estudo surge na sequência de um trabalho prévio que incluiu a anotação dos registos de vídeo de uma experiência com utilizadores realizada anteriormente. Com base na versão preliminar de análise do trabalho anterior, foi proposto um esquema de classificação de eventos que engloba tipos de interação relativos ao navegador, motor de pesquisa e páginas web. Cada tipo de interação é composto por eventos que, por sua vez estão associados a variáveis de análise. Dentro deste esquema, foram construídos módulos para analisar as interrogações de pesquisa submetidas. Com base neste esquema, foi revista a anotação dos vídeos e foi realizada a análise de dados de forma descritiva e inferencial. Os principais resultados demonstram que o grupo de baixa literacia em saúde utilizou sobretudo a caixa do motor de pesquisa e a funcionalidade de voltar atrás; interagiu mais tempo com página de resultados do motor de pesquisa, clicando mais com o botão esquerdo do rato e fazendo scrolling. Por outro lado, o grupo de alta literacia em saúde utilizou mais a barra de endereço e a funcionalidade de selecionar o texto do URL. Na página de resultados do motor de pesquisa este grupo fez mais cliques com o botão direito. A nível de reformulação de interrogações, que ocorrem no contexto da mesma necessidade de informação, os utilizadores com baixa literacia em saúde usaram mais as reformulações "totalmente novas", ou seja, sem termos em comum com a interrogação anterior. Por sua vez, o grupo de alta literacia em saúde fez mais reformulações.Health Literacy is "the level by which individuals can obtain, process, understand and communicate health-related information necessary to make informed health decisions". Users with a low level of health literacy are less aware of their medical conditions, more difficult to follow instructions and understand doctors' information. Increasingly, people turn to the web to search for health information. Low literacy users' difficulties in the real world are likely to continue to exist in the virtual world. The main objective of this study is to analyze the search behavior of users with different levels of health literacy. It intends to identify differences between people with low and high health literacy that can then be used to improve retrieval systems and contribute, among others, to facilitate access to information and education by people with low literacy. This study follows a previous work that included annotating video records of experience with users previously carried out. Based on the preliminary analysis version of the previous work, an event classification scheme was proposed that includes types of interactions related to the browser, search engine, and web pages. Each type of interaction is composed of events that, in turn, are associated with analysis variables. Within this scheme, modules were built to analyze the formulation of search queries. Based on this scheme, the annotation of the videos was revised, and the data analysis was performed in a descriptive and inferential manner. The main results demonstrate that the low health literacy group used mainly the search engine box and the backward feature. On the search engine results page, they clicked more with the left mouse button. On the results page, they spent more time on the interaction, mainly scrolling. On the other hand, the high health literacy group made more use of the address bar and the functionality of selecting the URL text. On the search engine results page, this group made more right-clicks. At the level of reformulations, which occur in the context of the same need for information, users with low health literacy used more "totally new" reformulations, that is, without terms in common with the previous question. In turn, the high health literacy group did more reformulations

Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search

High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for relevant medical literature is a key phase in constructing systematic reviews and often involves domain (medical researchers) and search (information specialists) experts in developing the search queries. Queries in this context are highly complex, based on Boolean logic, include free-text terms and index terms from standardised terminologies (e.g., the Medical Subject Headings (MeSH) thesaurus), and are difficult and time-consuming to build. The use of MeSH terms, in particular, has been shown to improve the quality of the search results. However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query. Naturally, the full value of the MeSH terminology is often not fully exploited. This article investigates methods to suggest MeSH terms based on an initial Boolean query that includes only free-text terms. In this context, we devise lexical and pre-trained language models based methods. These methods promise to automatically identify highly effective MeSH terms for inclusion in a systematic review query. Our study contributes an empirical evaluation of several MeSH term suggestion methods. We further contribute an extensive analysis of MeSH term suggestions for each method and how these suggestions impact the effectiveness of Boolean queries.Comment: This paper is currently in submission with Intelligent Systems with Applications Journal Technology-Assisted Review Systems Special issue and is under peer review. arXiv admin note: text overlap with arXiv:2112.0027

Search strategy formulation for systematic reviews: Issues, challenges and opportunities

Systematic literature reviews play a vital role in identifying the best available evidence for health and social care research, policy, and practice. The resources required to produce systematic reviews can be significant, and a key to the success of any review is the search strategy used to identify relevant literature. However, the methods used to construct search strategies can be complex, time consuming, resource intensive and error prone. In this review, we examine the state of the art in resolving complex structured information needs, focusing primarily on the healthcare context. We analyse the literature to identify key challenges and issues and explore appropriate solutions and workarounds. From this analysis we propose a way forward to facilitate trust and to aid explainability and transparency, reproducibility and replicability through a set of key design principles for tools to support the development of search strategies in systematic literature reviews