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Sarah Young

Principal Librarian Carnegie Mellon University

  • Pittsburgh PA

Sarah Young focuses on methods for study identification in evidence synthesis.

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Biography

Sarah Young is a social sciences librarian, providing teaching and research support to the Heinz College, Information Systems and CMU Africa. She holds graduate degrees in library and information science and development and international relations, and a certificate in program evaluation. Young provides methodological expertise for evidence synthesis (such as systematic and scoping reviews) in the social sciences, conducts research on evidence synthesis methods and develops and conducts evidence synthesis training locally and abroad.

Areas of Expertise

Evidence Synthesis
Systematic Review
Scoping Review
Information Retrieval
Information Literacy

Media

Social

Industry Expertise

Public Policy
Political Organization

Education

Michigan State University

Graduate Certificate

Program Evaluation

2022

University of Pittsburgh

M.L.I.S.

Health Resources and Services

2011

Aalborg University

M.Sc

Development and International Relations

2007

Articles

The Evidence Synthesis Institute: Building a high impact training program and supportive community for an emergent area of librarianship. Evidence Based Library and Information Practice

Evidence Based Library and Information Practice

2026

Objective – Seeing an absence of training opportunities for librarians supporting evidence synthesis outside of the health sciences, a collaborative team of librarians sought grant funding, developed a curriculum, and facilitated community building to provide this training.

Methods – The present case study describes the identification of a training gap, multi-faceted efforts made to fill that gap, and evaluation via pretests and post-tests to continuously improve the program.

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AI and automation in evidence synthesis: An investigation of methods employed in Cochrane, Campbell Collaboration, and Environmental Evidence reviews

Cochrane Evidence Synthesis and Methods

2025

Automation, including Machine Learning (ML), is increasingly being explored to reduce the time and effort involved in evidence syntheses, yet its adoption and reporting practices remain under-examined across disciplines (e.g., health sciences, education, and policy). This review assesses the use of automation, including ML-based techniques, in 2271 evidence syntheses published between 2017 and 2024 in the Cochrane Database of Systematic Reviews, and the journals Campbell Systematic Reviews, and Environmental Evidence. We focus on automation across four review steps: search, screening, data extraction, and analysis/synthesis.

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Searching and reporting in Campbell Collaboration systematic reviews: An assessment of current methods Campbell Systematic Reviews

Campbell Systematic Reviews

2024

The search methods used in systematic reviews provide the foundation for establishing the body of literature from which conclusions are drawn and recommendations made. Searches should aim to be comprehensive and reporting of search methods should be transparent and reproducible. Campbell Collaboration systematic reviews strive to adhere to the best methodological guidance available for this type of searching. The current work aims to provide an assessment of the conduct and reporting of searches in Campbell Collaboration systematic reviews.

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