Context:
This research explores impact, value and return on investment (ROI) of serving in high-level leadership roles within health sciences library (HSL) professional associations as perceived by the librarian leaders and the administrators to whom they report. The objective of the first phase of the study discussed here is to gather and share insights from a global range of library leaders on the value of such service to them as individuals and their institutions.
Methods:
The librarian survey targeted two audiences. First, individuals who served in leadership roles within MLA, CHLA/ABSC, EAHIL, AHLIA, and HLA over the past ten years. Second, current library directors in the Association of Academic Health Sciences Libraries Directory of U.S. and Canadian institutions. The 25-question survey asked about motivations and perceived benefits at key points in the leadership trajectory: when participants were invited to serve, after agreeing to run for office, and during their term. The online survey included closed and open-ended questions to capture a range of experiences.
Two independent analysts coded the open-ended responses, compared codes to establish a codebook, and then re-coded the data. We examined the codes to come to consensus on the themes and identify exemplar quotes.
Discussion:
From the literature and our own experiences, we anticipated themes related to professional growth, institutional visibility, and strategic alignment. Preliminary findings will be shared to foster dialogue on the value of leadership contributions within the HSL profession and to inform those considering future service.

Introduction
Since April 2022, indexing of MEDLINE records is performed predominantly by algorithm, with occasional human intervention. Recent research has raised concerns about indexing algorithms’ capacity to accurately identify meaningful elements of publications. This identification is a necessary precondition to communicating them to searchers through indexing. Concerns have included the risk of the algorithm overlooking one or more relevant concepts, not making use of appropriately precise controlled vocabulary, or assigning incorrect terms due to rhetorical or technical language. The indexing algorithm from 2022-2024 (MTIA) was a rule-based system with exceptions added over time; a machine learning model (MTIX) was implemented in 2024. NLM Technical Bulletins have noted general improvements in MTIX compared to MTIA. Recent research has explored particularities of evocative language and found improvements in discerning human from nonhuman-animal subjects.
METHODS
This work will replicate and improve on a previous project that assessed overall MTIA indexing performance. Our study will assess MTIX performance by reviewing a large, recent and random sample of MTIX-indexed records to determine whether their main concepts are adequately represented.
Using a web-form displaying the journal, title and abstract of a record, our screeners will identify key concepts that, per their searching experience, would be used to retrieve it and similar records. After establishing agreement between screeners, we will compare the consensus-concepts to the indexing applied by MTIX. We will compare these findings to previous research, noting emergent trends or issues.

RESULTS & CONCLUSION
We intend to make our web-form freely available online. Other results are forthcoming.

Introduction:
As artificial intelligence (AI) becomes increasingly integrated into health education, research, and clinical practice, institutions are releasing guidelines and policies that impact how faculty, staff, and learners engage with these technologies. In this rapidly changing landscape, current and future healthcare practitioners must quickly familiarize themselves with these guidelines and policies to adapt and shape their use of AI. To support the development of AI literacy, this project aims to create and share learning materials that facilitate understanding and application of institutional AI policies.

Description:
The team reviewed AI-related guidelines from member institutions of the Association of Faculties of Medicine of Canada (AFMC), using ChatGPT to extract common elements and build an educational framework. Policies from select healthcare institutions were also analyzed, and additional themes were incorporated. In total, twelve key themes emerged.

For each theme, case-based learning scenarios were developed for diverse audiences, including students, clinicians, and researchers. Scenarios included details on population, context, use type, tool license level, and discussion prompts. Feedback from clinicians and educators helped ensure relevance and applicability. Scenarios were translated into French, and both versions are openly available.

Discussion:
Health sciences librarians are uniquely positioned to collaborate with faculty and learners in developing AI competencies and promoting best practices in information use. By leveraging open educational methods, this resource is designed to be adaptable across institutions, supporting engaging and policy-informed AI education for health and medical learners, faculty and staff.