Journal of the Medical Library Association
The Journal of the Medical Library Association (JMLA) is the premier journal in health sciences librarianship, dedicated to advancing the practice and research knowledgebase of health sciences librarianship and providing equitable opportunities for authors, reviewers, and editorial team members. Read issues of the JMLA prior to January 2016 on PMC
University Library System, University of Pittsburgh
Brenda Linares, MLIS, MBA, AHIP, Medical Library Association President, 2024-2025
In this profile, Brenda M. Linares, AHIP, Medical Library Association (MLA) president 2024-25 is introduced through a discussion of her contributions and commitment to growing the next generation of Latina library leaders. As the first Latina immigrant MLA president, she partnered with colleagues to build organizational structures to strengthen diversity, equity, and inclusion in multiple regional chapters of MLA. In addition to her focus on integrating her family into her professional engagements, Linares brought a strong business orientation from her bachelor’s degree in Finance and her Master of Business Administration to her MLA leadership and to her professional role as Associate Dean of Library Services, University of Missouri- Kansas City (UKMC) Libraries.
Copyright (c) 2026 Annabelle V. Nuñez, MA, Kate Flewelling, MLIS, AHIP-D, Emily Vardell, MLS, PhD, AHIP, Kristine M. Alpi, MLS, MPH, PhD, AHIP-D https://creativecommons.org/licenses/by/4.0
The I’s have it: everything needed to practice medical librarianship starts with an I
The medical or health sciences library professional vocabulary uses many words that start with an I. On the eve of the 60th anniversary of the Janet Doe Lectureship, this lecture highlights and summarizes the 15 lectures (27%) that have included an I in their titles. The most frequent I word was information; this word appeared in four lectures. Only one lecture used more than one I word in the title. A new I word incorporated in this lecture but not its title is Intelligence, Artificial. +Italics were used to emphasize I words within the lecture or titles of published works.
Copyright (c) 2026 Jean P. Shipman, AHIP, FMLA https://creativecommons.org/licenses/by/4.0
Copyright (c) 2026 Janis F. Brown https://creativecommons.org/licenses/by/4.0
Heather Nadean Holmes, AHIP, Medical Library Association President, 2025–2026
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.
Copyright (c) 2026 Shannon D. Jones https://creativecommons.org/licenses/by/4.0
Automated indexing in MEDLINE and the Medical Text Indexer (MTI), 2000–2025: a scoping review
Objectives: To synthesize and map literature on automated indexing of the biomedical literature, with a focus on the Medical Text Indexer (MTI) at the National Library of Medicine (NLM). We review the drivers, benefits, and challenges of automated indexing, evolution of the MTI from 2000-2025, and impacts on information retrieval in MEDLINE. Methods: We conducted a scoping review following the JBI Manual for Evidence Synthesis and reported findings using PRISMA-ScR and PRISMA-S. We searched several bibliographic databases, key journals, conference proceedings, and grey literature sources, with no restrictions on language or study design. Eligible publications were published 2000-2025 and focused on MTI development. Screening, data charting, and thematic analysis were conducted by multiple reviewers. Results: We included 64 publications, with most originating from the United States (n=53, 83%) and five from Canada (8%). Study methods included evaluation or comparative studies (65%), qualitative descriptions (25%), and mixed methods (11%). MTI evolved from a rules-based recommendation tool in 2002 to the neural network–based MTIX in 2024. Despite numerous enhancements to the MTI, human curation remains necessary for approximately one-third of records to correct inaccuracies, capture missed concepts, and address errors arising from figurative language or algorithmic biases. Conclusions: This review synthesizes twenty-five years of MTI research (2000–2025). Despite reduced indexing times and a markedly improved algorithm, the MTIX has not yet achieved full equivalence to human indexing. Our findings suggest searchers should watch for algorithmic ambiguities in their MEDLINE searching and adapt accordingly. Health sciences librarians should work with stakeholders, including authors, to shape future algorithmic indexing methods, outputs, evaluation and research.
Copyright (c) 2026 Dean Giustini, Alexandre Amar-Zifkin, Eileen Chen, Janice Y. Kung https://creativecommons.org/licenses/by/4.0
124th Annual Conference, Medical Library Association, Inc., Portland, OR, May 18-21, 2024
Copyright (c) 2025 Kate E. Corcoran https://creativecommons.org/licenses/by/4.0
Introducing information literacy and careers in librarianship to New York City high school students: a case report
Background: Black, Indigenous, and People of Color (BIPOC) are underrepresented in librarianship. We developed a curriculum to introduce high school students from BIPOC communities to careers in health sciences librarianship and to concepts and skills in health information literacy. Case Presentation: Librarians at Weill Cornell Medicine partnered with Mentoring in Medicine, a Bronx-based nonprofit, to develop the Community Health Ambassador Program (CHAmP) curriculum. The project settings were at three New York City schools. The project took place over two years, with the pilot taking place in year 1, followed by an assessment which included data from pre- and posttests measuring student learning and focus groups with the students to gather feedback about their experience. Informed by data from the year one assessment, we worked with an experienced high school teacher to redesign the curriculum for year 2. Changes included reducing the amount of lecture and providing more time for activities to reinforce the content, including a team final project and presentations. In year two focus groups, students demonstrated increased understanding of health sciences librarianship and indicated increased engagement and enjoyment of the course compared to year 1. There was a statistically significant improvement in the mean student score from the pretest to the posttest. Conclusions: The revised curriculum resulted in increased student engagement and statistically significant improvement in learning compared to the year one pilot. We have published the full curriculum online under a Creative Commons license so that other organizations may implement it in their communities.
Copyright (c) 2026 Andy Hickner, Diana Delgado, Jessica Boyle, Lynne Holden, Sarah Jewell, Michael A. Wood, Terrie R. Wheeler https://creativecommons.org/licenses/by/4.0
Advancing data-driven health research from the All of Us data training and engagement program
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.
Copyright (c) 2026 Kristi Sadowski; DeBran Tarver; Jennifer Burnette; Allissa Dillman; Regina Renfro; LaFrancis Gibson; Kelli Bursey; Laura Bartlett; Nadine Bonds-Bishop; Lisa Connor; Efraín Flores-Rivera; Sarah Joseph; Yuqi He; Margaret Henderson; Savannah L. Kelley; Maletta Payne; Brett Porter; Kimberly Prosper; Melissa Rethlefsen; Deborah J. Rhue; Dede Rios; Anna Simonson; Jocelyn Swick-Jemison; Linda Todd; Shelby Watson; Semhar Yohannes https://creativecommons.org/licenses/by/4.0
Semi-automated rubrics for evidence-based medicine assessment: a case report on grading time reduction
Background: Librarians providing feedback on Evidence-Based Medicine (EBM) assignments face time constraints. This case report describes implementing a semi-automated rubric — defined here as a tool that automatically calculates scores from grader-selected ratings while narrative feedback is still written by the instructor — to reduce grading time for an EBM capstone assignment for first-year medical students. Case Presentation: Two cohorts of approximately 100 students completed the same EBM assignment. The 2023 cohort received manual feedback, while the 2024 cohort received feedback using the semi-automated rubric. Grading time was logged for both cohorts. The rubric, adapted from a template, automatically calculates scores based on selected criteria, streamlining the feedback process. Conclusions: The semi-automated rubric reduced grading time by 30%, from an average of seven minutes per assignment to five minutes. This simple, adaptable intervention can help reduce educator workload, improve feedback timeliness, and enhance assessment consistency. While limited by its single-grader and single institution design, this case report offers practical insights for educators seeking to improve feedback efficiency in EBM and other disciplines.
Copyright (c) 2026 Juliana T. Magro https://creativecommons.org/licenses/by/4.0
Building an interactive dashboard to visualize institutional open access publishing trends
As part of an effort to seek sustainable support models for Open Access (OA) publishing, the University of Maryland, Baltimore (UMB), Health Sciences and Human Services Library’s (HSHSL’s) Scholarly Communications Committee developed an interactive dashboard to visualize university-wide OA publishing trends. Using publication data exported from Scopus and visualized in Microsoft Power BI, the dashboard displays five years of publishing trends by OA model, publisher, journal, school, and citation count. The dashboard is fully interactive, allowing users to filter results based on school, OA model, and year. The design of the dashboard was iterative, with planning discussions taking place in Summer 2024, data model development and initial data collection in Fall 2024, refining of the visualization and data model in early Spring 2025, and the publication of the final dashboard to our website in April 2025. The dashboard continues to be refined and improved based on feedback from stakeholders, and the project team plans to incorporate data on publishing costs in Spring 2026. The project was designed for sustainability and adaptability, with a documented workflow that will be easy for future committees to implement. This innovative, replicable approach supports informed decision-making around OA publishing and provides a model that can be adopted by other academic health sciences libraries.
Copyright (c) 2026 Emily Gorman, Amy Yarnell, Nicole Shelawala https://creativecommons.org/licenses/by/4.0
Evaluating automated or artificial intelligence search tools for evidence synthesis
To advance information retrieval science for producing evidence syntheses at Canada’s Drug Agency, the Research Information Services team developed a replicable process to evaluate automated or artificial intelligence (AI) search tools. The team inventoried 51 tools in the fall of 2023 and built a flexible evaluation instrument to inform adoption decisions and enable comparison between tools. Building on this foundational evaluation work, the team further conducted a comparative analysis on three top-ranked tools in the fall of 2024. The investigation confirmed that these automated or AI tools have inconsistent and variable performance for the range of information retrieval tasks performed by Information Specialists at Canada’s Drug Agency. Implementation recommendations from this study informed a “fit for purpose” approach where Information Specialists leverage automated or AI search tools for specific tasks or project types.
Copyright (c) 2026 Robin Featherstone https://creativecommons.org/licenses/by/4.0
A generalist institutional data repository offering both open and restricted access to support NIH data sharing compliance
In response to the 2023 NIH Data Management and Sharing (DMS) Policy, Washington University School of Medicine in St. Louis launched Digital Commons Data@Becker, a generalist institutional data repository supporting both open and restricted access to research data. Managed by Bernard Becker Medical Library’s DMS Team, the repository offers a fully mediated curation workflow that guides researchers through consultation, metadata capture, documentation, and quality control. Draft Digital Object Identifiers (DOIs) can be issued once access type is determined, with final DOI publication following curation and QC. Restricted datasets require Human Research Protection Office (HRPO) review and Data Transfer and Use Agreements (DTUAs), while open access datasets are freely downloadable. The repository leverages persistent identifiers such as Open Researcher and Contributor ID (ORCID iDs), Research Organization Registry (ROR) IDs, and DOIs, along with the DataCite metadata schema and custom metadata fields. Since its launch in 2023, Digital Commons Data@Becker has published 30 datasets spanning biomedical imaging, sequencing, quantitative assays, flow cytometry, and qualitative survey data. Across all datasets, there have been 4,409 views and 4,120 files downloaded, with restricted datasets generating 13 access requests, three of which were granted through DTUAs. Researchers emphasize the value of free institutional curation, flexible access models, and rapid DOI assignment. Digital Commons Data@Becker demonstrates how a generalist institutional data repository can balance accessibility and security to support NIH compliance, while advancing FAIR (Findable, Accessible, Interoperable, Reusable) data sharing and long-term stewardship.
Copyright (c) 2026 Seonyoung Kim, Xing Jian, Marcy Vana https://creativecommons.org/licenses/by/4.0
Blogs as data: using XQuery for content evaluation
Circulating Now, the history of medicine blog for the National Library of Medicine (NLM), highlights blog posts written by community contributors. To evaluate the community represented within the blog, the project team explored how XQuery, a language for querying XML data, could be utilized in developing a dataset on institutions represented in the blog. The team used ChatGPT to develop the XQuery script and processed the queries through BaseX. The resulting data was transferred to Excel where additional data elements, such as geographic location and institutional type, were manually added. From this dataset, the team created visualizations in Tableau to show the over 400 unique institutions across the world represented. These visualizations supplemented an internal report for the Circulating Now Editorial Board, illustrating the current engagement reach of the blog and areas for future possible collaboration.
Copyright (c) 2026 Eli Wachter, Elizabeth Mullen https://creativecommons.org/licenses/by/4.0
Fostering collaboration through learning communities: a case report on engaging with All of Us data among library professionals, faculty, and students
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.
Copyright (c) 2026 Yuqi He, Zachary Mcniece, Dawn Hackman, Nick Szydlowski https://creativecommons.org/licenses/by/4.0
Comparing five generative AI chatbots’ answers to LLM-generated clinical questions with medical information scientists’ evidence summaries
Objective: To compare answers to clinical questions between five publicly available large language model (LLM) chatbots and information scientists. Methods: LLMs were prompted to provide 45 PICO (patient, intervention, comparison, outcome) questions addressing treatment, prognosis, and etiology. Each question was answered by a medical information scientist and submitted to five LLM tools: ChatGPT, Gemini, Copilot, DeepSeek, and Grok-3. Key elements from the answers provided were used by pairs of information scientists to label each LLM answer as in Total Alignment, Partial Alignment, or No Alignment with the information scientist. The Partial Alignment answers were also analyzed for the inclusion of additional information. Results: The entire LLM set of answers, 225 in total, were assessed as being in Total Alignment 20.9% of the time (n=47), in Partial Alignment 78.7% of the time (n=177), and in No Alignment 0.4% of the time (n=1). Kruskal-Wallis testing found no significant performance difference in alignment ratings between the five chatbots (p=0.46). An analysis of the partially aligned answers found a significant difference in the number of additional elements provided by the information scientists versus the chatbots per Wilcoxon-Rank Sum testing (p=0.02). Discussion: Five chatbots did not differ significantly in their alignment with information scientists’ evidence summaries. The analysis of partially aligned answers found both chatbots and information scientists included additional information, with information scientists doing so significantly more often. An important next step will be to assess the additional information, both from the chatbots and the information scientists for validity and relevance.
Copyright (c) 2025 Mallory N. Blasingame, Taneya Y. Koonce, Annette M. Williams, Jing Su, Dario A. Giuse, Poppy A. Krump, Nunzia B. Giuse https://creativecommons.org/licenses/by/4.0
Board exam preparation resource trends in academic health sciences libraries serving colleges of osteopathic medicine programs
Background: The study seeks to identify the board exam preparation (prep) resources made available to medical students by academic health sciences libraries and other departments within U.S. colleges of osteopathic medicine (COM) programs, and the extent to which these resources are utilized by medical students and educators. Methods: The study was conducted in two phases. In phase one, forty-two COM library directors were invited to complete a survey regarding their usage, funding, and access models for available board prep resources. In phase two, semi-structured interviews were conducted via Zoom to explore the library's role in providing access to these resources. Results: In phase one, thirty-five out of a possible forty-two survey respondents shared the top board exam prep resources to which their COM programs subscribed, as well as the top library resources that included board prep elements. In phase two, thirteen directors participated in Zoom interviews, which were recorded and transcribed to identify key trends in funding responsibility, resource selection, and librarian involvement. It was revealed that governance matters, digital format rules, partnerships drive impact, telling the data story is key, and one-stop guidance works. Conclusion: The two-phase study provided a comprehensive review of the specific resources that academic health sciences libraries and other departments offer for board prep within COM programs. Through analyses of survey and semi-structured interview data, this study identifies trends in resource availability and use, informing strategic planning for libraries, medical school administration, and library science education, ultimately enhancing student outcomes.
Copyright (c) 2026 Joanne Muellenbach, Kyle Robinson, Hsinliang Chen, Harold Bright, Lori Fitterling https://creativecommons.org/licenses/by/4.0
Copyright (c) 2025 Endah Fitriasari https://creativecommons.org/licenses/by/4.0
Perceptions of digital medical information services applying new technologies
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.
Copyright (c) 2026 Jee Yeon Lee, Go eun Lee; JUNGSUN Seo; YEJIN CHOI https://creativecommons.org/licenses/by/4.0
Operational developments at the Journal of the Medical Library Association: improved production timelines, new policy introduction, and senior editorial team updates
In our editorial in the January/April 2023 issue of JMLA, we spoke of the challenges we faced when we took on the co-lead editor roles. At the end of that editorial, we stated our intention to get the publishing schedule back on track and to finally tackle other projects. And while it took us some time to report it, we are pleased to share that, in the publication year of 2024, JMLA resumed its regular quarterly publishing schedule.
Copyright (c) 2025 Jill T. Boruff, AHIP, Michelle Kraft, AHIP, FMLA https://creativecommons.org/licenses/by/4.0
Copyright (c) 2025 Farhad Shokraneh https://creativecommons.org/licenses/by/4.0
Guide to sound teeth: a syllabus-informed instructional session to provide targeted insights into the history of dentistry
Background: This case report details an exploratory instructional session for dental students led by librarian-instructors at the University at Buffalo. Using historical source materials from the Robert L. Brown History of Medicine Collection, an hour-long session was developed to introduce year-one dental students to the history of their profession and its ongoing collaboration with important clinical populations. Case Presentation: At the request of course faculty, the University at Buffalo’s Dental Liaison Librarian, History of Medicine Curator, and History of Medicine Archivist were invited to develop and lead a session on the history of dentistry for a first-year course, Profession, Practice, and Community Dentistry (PDO 801). A core feature of this course is the introduction of students to eight underserved dental patient populations—referred to as “communities of focus.” To supplement student learning, library staff utilized the holdings of the Robert L. Brown History of Medicine Collection to bring together stories, artifacts, and printed materials that spoke not only to the history of the profession, but also to the history of the communities of focus. Thought prompts were developed to guide students during a textual analysis activity that analyzed representative materials. Conclusions: Overall, this interdisciplinary collaboration provided the opportunity to develop and implement a syllabus-informed historical instructional session that offered targeted insights into dentistry’s past. Through guided discussions, hands-on exploration, and textual analysis of historic materials, instructors worked to inspire and educate participating dental students as they progress further along their path as providers of patient-forward care.
Copyright (c) 2025 Jessica Hollister, Keith Mages, Thomas Murphy, Meelin Dian Chin Kit-Wells https://creativecommons.org/licenses/by/4.0
Comparing the performance of narrow vs. broad search strategies when using machine learning-based software for title/abstract screening
Objective: To retrospectively evaluate workload implications and recall performance of narrower or broader database search strategies when using active learning screening tools. Methods: A convenience sample of 10 completed reviews was used to assess search strategy performance in ASReview LAB, an open-source systematic review software tool. For each review, a single database search strategy was selected and then revised to either broaden (n = 9) or narrow (n = 1) the scope. Results from both the more sensitive (broader) and more precise (narrower) search strategies were labeled as relevant or irrelevant based on inclusion in the completed review. The labeled result sets were uploaded into the ASReview LAB simulation module, which mimics the process of human screening. Metrics such as number of records screened to reach true recall of 95% or more were recorded. The effects of three different stopping rules on workload and recall were also explored. Results: For quantitative systematic reviews, the difference in absolute screening time required to reach 95% recall between broader or narrower search strategies was minimal (≤35 minutes). In contrast, for qualitative systematic reviews and other review types, broader search strategies led to increased workload. With respect to stopping rules, the time-based stopping heuristic resulted in substantial workload increases when broader search strategies were employed. Conclusions: Time savings achieved through the use of semi-automated screening tools may not always offset additional screening time required by broader, more sensitive search strategies. Librarians and information specialists should consider a variety of factors when determining the appropriate balance between search sensitivity and specificity in the context of semi-automated screening tools.
Copyright (c) 2025 Michelle Swab https://creativecommons.org/licenses/by/4.0
Standards for vision science librarians: 2026 review and revisions
The Association of Vision Science Librarians (AVSL) is an international organization composed of professional librarians, or persons acting in that capacity, whose collections include vision-related materials and/or whose patrons include vision scientists. Since 1976, AVSL has defined standards for vision science libraries in all capacities, including optometry, ophthalmology, academia, industry, and beyond. As the most recent Standards for Vision Science Libraries was published in 2014, an AVSL member committee was formed in 2024 to review the Standards and consider what a revision should include. Over the course of several months, committee members collaborated with one another, held discussions with other AVSL members, and reviewed current literature. What resulted is a revised set of Standards that aims to address the significant changes that have taken place not only within the field of librarianship, but with the information needs of the vision science professions as well. These updated Standards place the focus on the librarian rather than the library, as many librarian positions that once focused solely on vision science are now expected to manage several disciplines. The aim of this revision is to serve as a starting point for any librarian working with vision science materials and/or serving vision scientist patrons who wishes to develop their knowledge base and skills in this particular field of health sciences librarianship.
Copyright (c) 2026 Heather Edmonds, Karen S. Alcorn, Caroline Allen, Rudy Barreras, Dede Rios, PhD, MS , Leslie Holland, Thandavarayan Kumaragurupari https://creativecommons.org/licenses/by/4.0
How ChatGPT writes scientific titles in medical research: structural and content differences compared to human authors
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.
Copyright (c) 2026 Paul Sebo, Bing Nie, Ting Wang https://creativecommons.org/licenses/by/4.0
Structure and elements of library evidence synthesis services: a content analysis of publicly available information
Objective: Through a content analysis of public websites, this paper provides a centralized and summarized picture of evidence synthesis (ES) service characteristics at academic libraries and medical schools and centers in the United States in the mid-2020s. Methods: Our team identified 116 institutions classified in the 2021 Carnegie Classification as R1, R2, or Medical Schools and Centers that had an ES service. These services were coded for service characteristics. Using frequency and comparison tables, we answered the following research questions: What are the common structures and characteristics of ES services, within and independent of Carnegie Classification of Research Activity? Is there any relationship between Carnegie Classification of Research Activity and Size & Setting and characteristics of ES services? Results: We found that services often used tiers to delineate between services offered, with higher tiers requiring more responsibilities from the library or information professional. Some universities and medical schools limit librarian involvement with ES projects based on user role (student, faculty, researcher, etc.) or user affiliation with a college or department. Librarian acknowledgment or authorship are common in return for engagement in ES projects. Fees and Memoranda of Understanding were uncommon but do exist at some institutions. Conclusion: Librarians seeking to create or update their ES service can benefit from this ‘data snapshot,’ as it will allow them to see the service elements under consideration by other institutions and offer ideas for their own services. Overall, we found that there were more similarities than differences between basic ES service structures (e.g. tier amounts/categories, presence of fees, etc.), independent of classification type or size setting. We recommend that librarians utilize this paper and the associated data in order to identify which institutional elements are important in their context for ideas in improving their ES service.
Copyright (c) 2026 Jaena Manson, Anna White, Emily Metcalf https://creativecommons.org/licenses/by/4.0