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

ABSTRACT

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.

KEYWORDS:

• Machine learning
• natural language processing
• semi-automation
• evidence synthesis
• health psychology
• systematic review

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors are supported by the National Institutes of Health (NIH) under the National Library of Medicine (NLM) award R01LM012086 (Wallace and Marshall), the Science of Behavior Change Common Fund Program through an award administered by the National Institute on Aging, U.S. PHS grant 5U24AG052175 (Johnson), the U.S. National Science Foundation (NSF) grants 1743418 and 1843025 (Rajasekaran), and the UK Medical Research Council (MRC), through its Skills Development Fellowship program, grant MR/N015185/1 (Marshall). The views presented here are solely the responsibility of the authors and do not necessarily represent the official views of the NIH or other governing bodies.