Session: Systematic Review and Meta-analysis: A Hands-on Workshop (Society for Social Work and Research 27th Annual Conference - Social Work Science and Complex Problems: Battling Inequities + Building Solutions)

All in-person and virtual presentations are in Mountain Standard Time Zone (MST).

SSWR 2023 Poster Gallery: as a registered in-person and virtual attendee, you have access to the virtual Poster Gallery which includes only the posters that elected to present virtually. The rest of the posters are presented in-person in the Poster/Exhibit Hall located in Phoenix A/B, 3rd floor. The access to the Poster Gallery will be available via the virtual conference platform the week of January 9. You will receive an email with instructions how to access the virtual conference platform.

RMW-3 Systematic Review and Meta-analysis: A Hands-on Workshop

Thursday, January 12, 2023: 8:00 AM-12:00 PM
Maryvale B, 2nd Level (Sheraton Phoenix Downtown)
Ruopeng An, PhD, Brown School, Washington University in St. Louis
pre-specified eligibility criteria to answer a specific research question. Meta-analysis is a quantitative, formal, epidemiological study design used to systematically assess the results of previous research to derive conclusions about that body of research. All meta-analyses should be based on studies identified from a systematic review, but not every systematic review contains a meta-analysis. It is because the designs of the studies may be too different, the outcomes measured may not be sufficiently similar, or there may be concerns about the quality of the studies for an average result across the studies to be meaningful.

This workshop introduces the key concepts and techniques in systematic review and meta-analysis through case study-based presentations and hands-on programming demonstrations. By the end of the workshop, participants should be able to (1) Layout a systematic review protocol, (2) Define inclusion and exclusion criteria, (3) Design a keyword search algorithm and implement it in multiple databases; (4) Design a data extraction tool; (5) Use a study quality or bias assessment tool; and (6) conduct a fixed/random effect meta-analysis on a continuous or binary outcome using R.

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