# CHLA 2025 CONFERENCE PANELS / ABSC CONGRÈS 2025 PANELS

## TL;DR

This panel discusses how open science and search filters can improve healthcare research in Canada by enhancing data accessibility and evidence retrieval for diverse populations.

## Contribution

The panel introduces strategies for using federated data systems and validated search filters to improve data discoverability and inclusivity in health research.

## Key findings

- Persistent identifiers and federated systems are vital for data privacy and interoperability in collaborative health research.
- Validated search filters can improve retrieval efficiency for evidence on underrepresented populations.
- Incorporating insider input and pilot testing enhances the relevance and accuracy of search filters.

## Abstract

This panel will explore the critical role of open science principles in transforming healthcare research in Canada. A diverse panel will share their expertise: a research data librarian will discuss the data discoverability process in Canada and the role of persistent identifier infrastructure in supporting linked data; a data scientist will provide insights into the technical and ethical considerations of implementing federated data systems and leveraging open data for research; and a health research professional will present strategies for enhancing data accessibility and interoperability within the Canadian healthcare research landscape. Following these brief presentations, a moderated discussion led by a World Data System representative will delve deeper into: a) the significance of federated health data systems, which enable collaborative research while preserving data privacy and security; b) the crucial role of persistent identifiers in ensuring data discoverability and interoperability; and c) how open science practices, including data sharing, transparency, and reproducibility, can accelerate medical breakthroughs, improve patient outcomes, and foster a more equitable and innovative healthcare system.

Efficiently retrieving relevant evidence from databases is crucial for knowledge syntheses, particularly for underrepresented or hard-to-define populations. This panel explores the development, validation, and use of search filters for locating research on specific demographic groups, such as age groups, minority populations, or workers in particular settings. Through three presentations and an interactive conversation with the panelists, we will discuss how and when to use validated and unvalidated search filters to improve retrieval efficiency for evidence relating to the health of population groups. We will discuss the principles of filter design, focusing on terminology, inclusion/exclusion criteria, and balancing sensitivity and precision. Through examples of validated filters for minority populations, we will explore how to design filters to reduce irrelevant results while maintaining inclusivity for evolving terminology. We will review methods to ensure relevance and accuracy, including insider input, pilot testing, relative recall from published reviews, and flexibility in updating for different purposes or projects. Practical applications in academic, clinical, and policy contexts will be addressed, alongside challenges like evolving language, limited metadata specific to the population, and translating filters for use in different search interfaces. We will also discuss how information specialists share filters through publication and repositories. Inspired by resources such those provided by ISSG and the University of Alberta Library, we suggest options for finding, adapting, and citing search filters. By covering both theoretical foundations and practical applications, this panel aims to improve the retrieval of population-specific health evidence, offering tools to better serve diverse populations in healthcare and social research.

---
Source: https://tomesphere.com/paper/PMC12352439