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Twelve tips for data extraction for knowledge syntheses.

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Title: Twelve tips for data extraction for knowledge syntheses.
Authors: Maggio, Lauren A.1 (AUTHOR) lmaggio@uic.edu, Costello, Joseph A.1 (AUTHOR), Torre, Dario M.2 (AUTHOR), Gin, Brian3 (AUTHOR)
Source: Medical Teacher. Mar2026, Vol. 48 Issue 3, p364-372. 9p.
Subject Terms: *Medical education, *Information resources, *Information retrieval, *Metadata, Support groups, Teams in the workplace, Consensus (Social sciences)
Abstract: In medical education, the number of knowledge syntheses has increased dramatically, reflecting their growth and influence on education practice, research, and policy. However, despite the availability of instruction on many of the steps of conducting knowledge syntheses, practical guidance for the critical step of data extraction is limited. Data extraction is the process of systematically identifying and collecting information from the studies included in a knowledge synthesis. Without clear guidance, data extraction can become flawed and overly time-consuming, ultimately jeopardizing the quality of the knowledge synthesis. This article addresses this gap by offering 12 practical tips for data extraction. The tips are grounded in the literature and informed by the authors' collective experience conducting and mentoring knowledge synthesis projects. Organized into two sections, creating a data extraction tool and operationalizing it, the tips provide actionable guidance on aligning extraction with research objectives, supporting a team-based approach, resolving discrepancies, and how to pilot a data extraction tool. Taken together, these tips aim to improve the rigor, efficiency, and reliability of knowledge synthesis in medical education. [ABSTRACT FROM AUTHOR]
Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: In medical education, the number of knowledge syntheses has increased dramatically, reflecting their growth and influence on education practice, research, and policy. However, despite the availability of instruction on many of the steps of conducting knowledge syntheses, practical guidance for the critical step of data extraction is limited. Data extraction is the process of systematically identifying and collecting information from the studies included in a knowledge synthesis. Without clear guidance, data extraction can become flawed and overly time-consuming, ultimately jeopardizing the quality of the knowledge synthesis. This article addresses this gap by offering 12 practical tips for data extraction. The tips are grounded in the literature and informed by the authors' collective experience conducting and mentoring knowledge synthesis projects. Organized into two sections, creating a data extraction tool and operationalizing it, the tips provide actionable guidance on aligning extraction with research objectives, supporting a team-based approach, resolving discrepancies, and how to pilot a data extraction tool. Taken together, these tips aim to improve the rigor, efficiency, and reliability of knowledge synthesis in medical education. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/0142159X.2025.2551252
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      – Code: eng
        Text: English
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              Text: Mar2026
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