Background: Fulfilling a recognized need in data skills training for academic librarians, the National Library of Medicine and the National Institutes of Health All of Us Research Program collaborated to enhance academic library workers’ skills in biomedical and public health data, as well as their library's research capacity, through the awareness and use of the All of Us Researcher Workbench. Case Presentation: The All of Us Data Training and Engagement Program for Academic Libraries (ALP) blended professional development training, hands-on learning, and peer-to-peer networking that focused on increasing knowledge of the All of Us Researcher Workbench. Activities were designed to build institution-wide, interdisciplinary awareness and interest in using the All of Us Researcher Workbench. A series of ongoing activities ensured sustained skill-building and collaboration, and included training for R, a program language used for statistical computing and data visualization. Program activities were intentionally designed to help grow institutional research capacity, enhance skills in biomedical and public health data, and promote meaningful use of the Researcher Workbench to campus communities. Conclusion: The ALP helped participants overcome barriers to data access and improve research infrastructure and successfully empowered 115 library professionals to leverage the All of Us Researcher Workbench for meaningful biomedical and public health research. Measured outcomes validate the success of the program and demonstrate how the ALP has positioned participating institutions for long-term success in biomedical and public health research. Institutions can build upon the foundation established through this case report to advance equitable, data-driven health research across academic landscapes.
Background: Research data services (RDS) have expanded in academic libraries but can be challenging to develop, particularly in teaching-intensive and less-resourced institutions. Learning communities offer a promising model for building skills, fostering collaboration, and aligning services with local needs. Case Presentation: This case report describes the development and implementation of three learning communities—a library group, a faculty group, and a student group—at a teaching-focused institution. These communities brought together library professionals, faculty, and students from diverse disciplines—including health sciences, education, data science, and engineering—to collaboratively explore the All of Us dataset. By working with the same dataset, participants were able to move quickly from abstract concepts to hands-on practice, while developing a shared understanding of tools, workflows, and challenges. The learning communities also served as platforms for building institutional capacity in data-intensive research. Conclusions: The learning communities model proved to be an effective strategy for fostering cross-disciplinary collaboration, promoting data literacy, and building institutional readiness to support research using the All of Us dataset. By centering on local expertise, learning communities provide a sustainable, resource-conscious framework for developing RDS. This approach also demonstrates how academic libraries can act as conveners and catalysts for equitable data engagement. Lessons learned from this case may inform similar efforts at other institutions seeking to build collaborative, inclusive models for engaging with various data resources.
Objectives: This study examines users’ behaviors and perceptions when accessing medical information in digital environments and proposes strategic directions for medical libraries seeking to adopt emerging technologies. Specifically, we empirically investigate user expectations regarding digital technologies, differences in perception based on individual characteristics, and key factors influencing satisfaction with information use. Methods: We conducted a web-based survey from January to February 2024 through the National Center for Medical Science Knowledge, a national institution in South Korea. A total of 580 participants—including healthcare professionals, researchers, educators, and students—completed a questionnaire assessing digital information behavior, preferences for emerging technologies, perceptions of information and service quality, and overall satisfaction. We analyzed the data using descriptive statistics, analysis of variance (ANOVA), and multiple regression analysis. Results: Users exhibited a strong tendency to use online-based medical information, reporting particular interest in big data (38.5%) and artificial intelligence (24.9%) technologies. Statistically significant differences in preferences and awareness of digital technologies were observed based on gender, education level, academic major, and occupation. Information reliability (β = .623), service speed, and personalization were identified as key determinants of user satisfaction. Conclusion: Medical libraries should establish technology-based services that are suitable for the digital information environment and develop customized strategies that accurately reflect user characteristics and demands. This study offers practical insights for designing user-centered digital medical information services.
This profile offers a portrait of Heather Nadean Holmes, MLIS, AHIP, 2025-2026 Medical Library Association (MLA) President, as a leader defined by authenticity, courage, and deep commitment to the profession. Holmes, known for her candor and dedication to the profession, brings a career rooted in service, gained from her work in hospital, academic, and association environments, to the presidency. Her professional journey reflects a sustained commitment to evidence-based practice, research, and elevating librarians as essential partners in health care, education, and discovery. Holmes is widely recognized for her work as a clinical medical librarian and her national leadership in developing the MLA Research Agenda. Colleagues describe her as a truth-teller who leads with integrity, sets clear boundaries, and consistently uses her platform to support others, especially early- and mid-career professionals. Her presidency focuses on inclusivity, transparency, and member engagement during a significant transition for both the Association and the profession. Beyond titles and accomplishments, this biography captures Holmes’s humanity. Her resilience, mentorship, humor, and unapologetic passions, including her love of K-pop, are highlighted. Together, these qualities portray a president who leads with conviction, empathy, and joy.
Objective: Scientific article titles play a central role in shaping research visibility, interpretation, and discoverability. With the rise of large language models like ChatGPT, there is growing interest in using AI tools to support title generation, yet little is known about how AI-generated titles differ from those written by human authors. This study compared titles written by original authors to those generated by ChatGPT-4.0 from the same abstracts, focusing on structural and content-level differences. Methods: Fifty research articles published in 2000, before the advent of generative AI, were randomly selected from ten high-impact general internal medicine journals. For each, the structured abstract was submitted to ChatGPT-4.0 using a standardized prompt to generate a title. Human-written and AI-generated titles were then compared using quantitative measures (word and character counts, punctuation marks) and descriptive content analysis (study design, population descriptors, outcome emphasis, public health or clinical framing, temporal context). Paired statistical tests were applied to assess differences. Results: ChatGPT-generated titles were significantly longer than human-written titles (median 16 vs. 12.5 words, p-value<0.001) and included more characters and punctuation marks (colons in 100% vs. 30%; p-value<0.001). AI titles more often specified or clarified study design, detailed populations, emphasized outcomes, framed findings in public health or clinical terms, and incorporated temporal context. Conclusion: ChatGPT-4.0 produces more explicit and structured titles than human authors, emphasizing methodological clarity and content completeness. These findings raise important questions about norms in scientific communication and the need for further research and ethical guidance on AI-assisted writing.
