McGill University Library has a subscription to Covidence, a useful tool for importing database records, removing duplicate records, screening, documenting critical appraisal/risk of bias and data extraction, and exporting data. Covidence also allows members of the McGill community to invite external reviewers to join their review team.
For access and support for Covidence, please consult: https://support.covidence.org/help/mcgill-university-library
There are many resources that explain how to go about screening. One place to start is the guide on Screening Studies created by the librarians at the University of Toronto.
These librarians have also compiled example screening templates available in Open Science Framework.
Software packages specifically designed for knowledge synthesis (e.g., Covidence) will typically include a record screening/study selection function. This allows more than one reviewer to independently screen the records without seeing other reviewers' decisions to include or exclude studies, and thus reduces bias.
Some software tools also include screening prioritization, which allows you to re-sort the records left to screen by relevance, based on past decisions to include or exclude records already screened.
Partial list of software tools using machine learning/AI for screening prioritization:
The Systematic Review Tool Box is another useful resource for keeping up to date with screening tools:
Critical appraisal should involve an assessment of the risk of bias in the relevant studies and may also involve an assessment of how the studies were reported.
As librarians, we are generally not involved in the appraisal process, but we can provide guidance on finding critical appraisal tools if needed. They are generally specific to a given study design or research methodology. The following are some suggested tools but this list is not exhaustive and they may not have been validated.
Validity assessment tools for evidence synthesis: Your one-stop-shop (LATITUDES Network)
See Whiting P, Wolff R, Savovic J, Devine B, Mallett S. Introducing the LATITUDES network: A library of assessment tools and training to improve transparency, utility and dissemination in evidence synthesis. J Clin Epidemiol. 2024;174.
Thomson H, Campbell M. “Narrative synthesis” of quantitative effect data in Cochrane reviews: Current issues and ways forward [Internet]. Cochrane Learning Live Webinar Series 2020 Feb. https://training.cochrane.org/resource/narrative-synthesis-quantitative-effect-data-cochrane-reviews-current-issues-and-ways.
Campbell, M., McKenzie, J. E., Sowden, A., Katikireddi, S. V., Brennan, S. E., Ellis, S., Hartmann-Boyce, J., Ryan, R., Shepperd, S., Thomas, J., Welch, V., & Thomson, H. (2020). Synthesis without meta-analysis (SWiM) in systematic reviews: Reporting guideline. BMJ, 368, l6890. doi:10.1136/bmj.l6890
Hoaglin DC, Hawkins N, Jansen JP, Scott DA, Itzler R, Cappelleri JC, et al. Conducting indirect-treatment-comparison and network-meta-analysis studies: Report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: Part 2. Value Health. 2011;14(4):429-37.
Nakagawa S, Yang Y, Macartney EL, Spake R, Lagisz M. Quantitative evidence synthesis: a practical guide on meta-analysis, meta-regression, and publication bias tests for environmental sciences. Environmental Evidence. 2023;12(1):8.
Due to a large influx of requests, there may be an extended wait time for librarian support on knowledge syntheses.
Find a librarian in your subject area to help you with your knowledge synthesis project.
Or contact the librarians at the
Schulich Library of Physical Sciences, Life Sciences, and Engineering
schulich.library@mcgill.ca
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