With the diversification of Internet uses, online content type has become richer. Alongside organic results, search engine results pages now provide tools to improve information searching and learning. The People also ask (PAA) box is intended to reduce users' cognitive costs by offering easily accessible information. Nevertheless, there has been scant research on how users actually process it, compared with more traditional content type (i.e., organic results and online documents). The present eye-tracking study explored this question by considering the search context (complex lookup task vs. exploratory task) and users' prior domain knowledge (high vs. low). Main results show that users fixated the PAA box and online documents more to achieve exploratory goals, and fixated organic results more to achieve lookup goals. Users with low knowledge process PAA content at an early stage in their search contrary to their counterparts with high knowledge. Given these results, information system developers should diversify PAA content according to search context and users' prior domain knowledge.
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http://dx.doi.org/10.1016/j.apergo.2024.104367 | DOI Listing |
Health Qual Life Outcomes
December 2024
Centre for Outcomes Research and Evaluation, McGill University Health Centre-Research Institute, Montreal, QC, Canada.
Background: Health-related quality of life (HRQL) is an important endpoint when evaluating the effectiveness of interventions in people living with hip and knee osteoarthritis (OA). The aim of this study was to generate domains for a new OA-specific preference-based index of HRQL in people living with hip or knee OA.
Methods: The proposed HRQL index was based on a formative measurement model.
BMC Public Health
December 2024
Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Background: Research that investigates the negative health effects of stigma beyond the individual and interpersonal levels is increasingly using the concept of "structural stigma." This scoping review investigates how the concept of "structural stigma" has been used and operationalized in health-related literature to date in order to characterize its usage and inform future operationalizations.
Methods: A systematic search and screening process identified peer-reviewed, English-language research articles that used the term "structural stigma" available prior to January 1, 2024 in five databases (i.
BMC Med Res Methodol
December 2024
School of Public Health, Xuzhou Medical University, No. 209 Tongshan Road, Xuzhou, Jiangsu, 221004, China.
Background: The prediction of coronavirus disease in 2019 (COVID-19) in broader regions has been widely researched, but for specific areas such as urban areas the predictive models were rarely studied. It may be inaccurate to apply predictive models from a broad region directly to a small area. This paper builds a prediction approach for small size COVID-19 time series in a city.
View Article and Find Full Text PDFTransplant Cell Ther
December 2024
Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, USA 33612. Electronic address:
Background: Axicabtagene ciloleucel (axi-cel), a chimeric antigen receptor (CAR) T-cell therapy, has significantly improved clinical outcomes in adult patients with relapsed/refractory large B-cell lymphoma (LBCL). However, few studies have examined patient-reported outcomes (PROs) or neurocognitive performance in patients treated with axi-cel. Moreover, no longitudinal PRO study has reported on patients treated with axi-cel as standard of care in the United States, to our knowledge.
View Article and Find Full Text PDFNeural Netw
December 2024
School of Computer Science, Wuhan University, Luojiashan Road, Wuchang District., Wuhan, 430072, Hubei Province, China; Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, No. 8, Yangqiaohu Avenue, Zanglong Island Development Zone, Jiangxia District, Wuhan, 2007, Hubei Province, China. Electronic address:
The remarkable success of Graph Neural Networks underscores their formidable capacity to assimilate multimodal inputs, markedly enhancing performance across a broad spectrum of domains. In the context of molecular modeling, considerable efforts have been made to enrich molecular representations by integrating data from diverse aspects. Nevertheless, current methodologies frequently compartmentalize geometric and semantic components, resulting in a fragmented approach that impairs the holistic integration of molecular attributes.
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