SIG Home

SIG-HLTH Awards

Available awards, Application Criteria and Evaluation Criteria

SIG-HLTH Best Health-Related ASIS&T 2026 Annual Meeting Paper & Poster Awards (submissions due Monday, 8/31)

Call for Submissions:

SIG-HLTH Best Health-Related ASIS&T 2026 Annual Meeting Paper & Poster Awards (submissions due Monday, 8/31)

SIG-HLTH, ASIS&T’s Special Interest Group on Health Informatics, is pleased to open submissions for the SIG-HLTH Best Health-Related ASIS&T 2026 Annual Meeting Long Paper Award, the SIG-HLTH Best Health-Related ASIS&T 2026 Annual Meeting Short Paper Award, and the SIG-HLTH Best Health-Related ASIS&T 2026 Annual Meeting Poster Award. We define health broadly to encompass research related to health information behavior, health informatics, health information systems, etc.

The first author of each winning submission will receive up to $125 for the SIG-HLTH Best Long Paper Award, up to $75 for the SIG-HLTH Best Short Paper Award, and up to $50 for the SIG-HLTH Best Poster Award. Additionally, all authors of winning submissions will each receive an award certificate. In the case of a tie, winners will split the award. Please see below for more information about each of these awards.

SIG-HLTH Best Health-Related ASIS&T 2026 Annual Meeting Long Paper Award;

SIG-HLTH Best Health-Related ASIS&T 2026 Annual Meeting Short Paper Award:

These awards recognize the best health-related long paper and the best health-related short paper accepted for the ASIS&T 2026 Annual Meeting (or Virtual Satellite Meeting).

SIG-HLTH Best Health-Related ASIS&T 2026 Annual Meeting Poster Award

This award recognizes the best health-related poster accepted for the ASIS&T 2026 Annual Meeting (or Virtual Satellite Meeting).

Eligibility:

In order to win a SIG-HLTH award, authors must be ASIS&T members in good standing and must be a current member of SIG-HLTH. [To join SIG-HLTH (free for ASIS&T members!), go to https://www.asist.org/ and log in. Then click on “My Info,” and look at the list on the right. That list shows the groups you have joined. Click the plus sign

there to add SIG-HLTH.] Additionally, all submitted papers and posters must have already been accepted for inclusion in the ASIS&T 2026 Annual Meeting or Virtual Satellite Meeting. Members of the SIG-HLTH Executive Board are not eligible for these awards. Additionally, SIG-HLTH Executive Board members whose students apply for these awards must recuse themselves from adjudicating their own students’ submissions.

Criteria:

The SIG-HLTH Executive Board Members will independently rate each submission on the following dimensions and the Board will then meet to finalize their decision.

Theoretical framework and/or conceptual relevance to health (20 points)

Problem statement and appropriateness of the methods (20 points)

Significance and implications of the findings (20 points)

Originality (20 points)

Clarity of expression (10 points)

References (10 points)

Submission Process:

Please forward your ASIS&T acceptance email, attaching your anonymized paper or poster submission as a pdf file (please specify short paper, long paper, or poster in your file name), to Beth St. Jean (SIG-HLTH Treasurer) at bstjean@umd.edu by Monday, August 31, 2026. Within the body of your email, please copy/paste your paper or poster abstract, along with a 150- to 200-word description of why you feel your submission is a strong candidate for one of these awards.

Questions?:
Please feel free to email me at bstjean@umd.edu

We look forward to receiving your submission!

SIG-HLTH Best Health-Related ASIS&T 2025 Annual Meeting Paper and Poster Award Winners

SIG-HLTH Best Health-Related ASIS&T 2025 Annual Meeting Long Paper Award

Title: When Chatman Meets Chinese Rural Older People with Health Anxiety: From Life in the Round to Concentric Life Circles

Authors: Lin Wang, Xinyi Wu, and Huajian Zhu, Chinese Academy of Science and Education Evaluation, Hangzhou Dianzi University, China

Abstract: Rural Chinese older people face significant information poverty and elevated health anxiety, yet their health information behavior remains understudied in non-Western contexts. This study employs in-depth interviews, grounded theory, and information horizon mapping to investigate the health information seeking patterns of this vulnerable group. Our findings reveal a concentric life circle pattern, where health information behavior radiates outward from the core life circle (kinship ties/close community) to transitional life circles (extended networks). By integrating Chatman's small world theory and life in the round theory with China's localized context, we propose the original "Concentric Life Circle Theory" (CLCT). This theory advances cross-cultural information behavior research and offers actionable solutions to mitigate health anxiety through culturally tailored information interventions.

SIG-HLTH Best Health-Related ASIS&T 2025 Annual Meeting Short Paper Award

Title: Construction and Representation Learning of Social Heterogeneous Information Networks based on Multimodal Fusion and Enhanced-HGCN

Authors: Wei Zhou, Lu An, Ruilian Han, and Gang Li, Center for Studies of Information Resources, School of Information Management, Wuhan University

Abstract: During public health events, social media platforms serve as key channels for disseminating multimodal information especially from government departments. These data are crucial for enhancing public understanding and emergency preparedness. This study proposes a novel framework for constructing and learning representations from social heterogeneous information networks (SHINs) based on multimodal fusion and an Enhanced-HGCN (Hyperbolic Graph Convolutional Network) model. Approximately 74,403 flu-related microblog posts were collected, from which multimodal features were extracted to construct a heterogeneous network linking users, posts, and topics. Furthermore, the Enhanced-HGCN model with a two-layer graph convolution structure is proposed to learn node embeddings in the SHINs. Experimental results show that our approach significantly outperforms other baseline models including in clustering performance. This research validates the feasibility of multimodal SHINs construction and the effectiveness of the Enhanced-HGCN, providing a foundation for future applications such as knowledge recommendation and cross-platform information collaboration.

SIG-HLTH Best Health-Related ASIS&T 2025 Annual Meeting Poster Award

Title: Use of Eye-Tracking as a Method for Health Information Behavior Research

Authors: Sue Yeon Syn and Laura Lannan, Department of Information Sciences, The Catholic University of America

Abstract: This paper analyzes 25 health information behavior studies published in the period of 15 years (2009-2023) using eye-tracking as the research methodology. Eye-tracking technology has become a valuable tool to study information behavior. This study examines the ways of adopting eye trackers in health information behavior research and how the research is designed with an extended method the technology provides. This paper contributes to understanding methodological trends for health information behavior research.

Past Award Winners

SIG-HLTH Best Health-Related ASIS&T 2024 Annual Meeting Paper and Poster Award Winners

SIG-HLTH Best Health-Related ASIS&T 2024 Annual Meeting Paper Awards

Title: Standardizing Vehicle Travel Speed Data for Road Safety

Authors: Liliana Salas, College of Information Science and Arlie Adkins, College of Architecture, Planning & Landscape Architecture and the College of Public Health, University of Arizona, Tucson, AZ, USA

Abstract: In 2022, almost 50,000 people died in road crashes in the United States, with speeding implicated in 29% of these fatalities. Despite known links between vehicle speed and crash occurrence and severity, there are no federal guidelines for collecting vehicle travel speed (VTS) data. Cities with open VTS data are using unstandardized datasets, which complicates large-scale and cross-jurisdictional analysis. We conducted a qualitative assessment of open data repositories for the 25 largest U.S. cities, using a framework of knowledge representation, evaluated twelve metadata components, and determined the potential usability of these datasets. Our knowledge representation framework includes five data elements: speed metric, timestamp, geospatial representation, posted speed and vehicle type. Findings show that one-quarter of these cities have open VTS datasets. Of those cities, none has a VTS dataset containing all the elements defined in our framework. This suggests the need to design information policy standards for the collection and sharing of open VTS data.

Title: Exploring Young Adults' Mental Health Help-Seeking Journey: Preliminary Findings on Resource Navigation Behavior

Authors: Jiaying (Lizzy) Liu & Yan Zhang, School of Information, The University of Texas at Austin

Abstract: Young adults are among the most vulnerable populations susceptible to mental health concerns, yet they encounter various barriers in accessing resources and obtaining necessary support and treatment. While prior research has predominantly focused on technology utilization in mental health help-seeking, a more comprehensive understanding of how individuals navigate the broader landscape of available resources is crucial. To address this gap, we conducted in-depth interviews with 18 young adults to explore their lived experiences and resource navigation during the mental health help-seeking process. Guided by the Social-Ecological Framework, our study categorized the resources utilized by young adults into four categories: technological, interpersonal, community-based, and societal. Through thematic analysis, we report preliminary findings that identify specific characteristics of resources that young adults value and prioritize throughout their help-seeking journey. These findings provide implications for designing multi-level mental health resources and interventions to facilitate young adults' help-seeking. We also discuss methodological considerations to encourage in-depth conversations with marginalized groups on sensitive topics like mental health, emphasizing the importance of creating a safe, non-judgmental space for participants to share their experiences openly.

SIG-HLTH Best Health-Related ASIS&T 2024 Annual Meeting Poster Award

Title: The Role of Paying for Access in the Online Information Seeking-Behavior of Canadian Midwives: Preliminary Findings

Authors: Richmond Yeboah & Joan Bartlett, School of Information Studies, McGill University, Montreal, Quebec, Canada

Abstract: It is of paramount importance to support midwifery practice with research evidence. Nevertheless, Canadian midwives encounter significant obstacles in accessing research evidence for practice. As part of a broader study which seeks to identify an optimal method for providing midwives in Canada with access to online clinical resources, this poster reports on the preliminary findings of the survey phase. The objective of this study was to ascertain the frequency with which Canadian midwives utilize information sources accessible to them through institutional affiliations and to determine whether they are aware of the financial cost of providing such services. Additionally, the survey sought to ascertain whether their perception of a resource being free, or premium affects its perceived value and use intentions. A descriptive survey design employing a structured web-based questionnaire was used to collect the requisite data. The preliminary findings indicated that participants perceived the value of free sources to be lower than premium sources and that they intended to use premium sources more frequently if they were able to pay for them. Moreover, the study demonstrated that participants frequently seek information online to support clinical practice, yet they utilize access to online resources through their institutional affiliation less so.

SIG-HLTH Best Health-Related ASIS&T 2023 Annual Meeting Paper and Poster Award Winners

SIG-HLTH Best Health-Related ASIS&T 2023 Annual Meeting Paper Award

"What Is a Wave But 1000 Drops Working Together?": The Role of Public Libraries in Addressing Health Information Disparities For LGBTQIA+ Communities

Authors: Vanessa Kitzie, Nick Vera, and Valerie Vera, University of South Carolina, College of Information & Communications, School of Information Science, USA; Travis Wagner, University of Illinois, School of Information Sciences

Abstract: This paper presents results from a participatory action research study with 46 LGBTQIA+ community leaders and 60 library workers who participated in four community forums at public libraries across the US. The forums identified barriers to LGBTQIA+ communities addressing their health questions and concerns and explored strategies for public libraries to tackle them. Forums followed the World Café format to facilitate collaborative knowledge development and promote participant-led change. Data sources included collaborative notes taken by participants and observational researcher notes. Results revealed that barriers experienced by LGBTQIA+ communities are structurally and socially entrenched and require systematic changes. Public libraries must expand their strategies beyond collection development and one-off programming to meet these requirements. Suggested strategies include outreach, community engagement, and mutual aid initiatives characterized by explicit advocacy for LGBTQIA+ communities and community organizing approaches. Public libraries can readily adopt strategies overviewed in this paper for LGBTQIA+ health promotion.

SIG-HLTH Best Health-Related ASIS&T 2023 Annual Meeting Poster Award

Nonsuicidal Self-Injury and Content Moderation on TikTok

Author: Valerie Vera, University of South Carolina, College of Information & Communications, USA

Abstract: Online nonsuicidal self-injury communities commonly create and share information on harm reduction strategies and exchange social support on social media platforms, including the short-form video sharing platform TikTok. While TikTok's Community Guidelines permit users to share personal experiences with mental health topics, TikTok explicitly bans content depicting, promoting, normalizing, or glorifying activities that could lead to self-harm. As such, TikTok may moderate user-generated content, leading to exclusion and marginalization in this digital space. Through semi-structured interviews with eight TikTok users with a history of nonsuicidal self-injury, this research explores how users experience TikTok’s algorithm to create and engage with content on nonsuicidal self-injury. Findings demonstrate that users understand how to circumnavigate TikTok’s algorithm through algospeak and signaling to maintain visibility on the platform. Further, findings emphasize that users actively engage in self-surveillance and self-censorship to create a safe online community. In turn, content moderation can ultimately hinder progress toward the destigmatization of nonsuicidal self-injury and restrict social support exchanged within online nonsuicidal self-injury communities.