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Semi-Automated evidence synthesis in health psychology: current methods and future prospects

Research output: Contribution to journalArticle

Iain J. Marshall, Blair T. Johnson, Zigeng Wang, Sanguthevar Rajasekaran, Byron C. Wallace

Original languageEnglish
Pages (from-to)145-158
Number of pages14
JournalHealth Psychology Review
Issue number1
Publication statusPublished - 2 Jan 2020

King's Authors


The evidence base in health psychology is vast and growing rapidly. These factors make it difficult (and sometimes practically impossible) to consider all available evidence when making decisions about the state of knowledge on a given phenomenon (e.g., associations of variables, effects of interventions on particular outcomes). Systematic reviews, meta-analyses, and other rigorous syntheses of the research mitigate this problem by providing concise, actionable summaries of knowledge in a given area of study. Yet, conducting these syntheses has grown increasingly laborious owing to the fast accumulation of new evidence; existing, manual methods for synthesis do not scale well. In this article, we discuss how semi-automation via machine learning and natural language processing methods may help researchers and practitioners to review evidence more efficiently. We outline concrete examples in health psychology, highlighting practical, open-source technologies available now. We indicate the potential of more advanced methods and discuss how to avoid the pitfalls of automated reviews.

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