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.

中文翻译：

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健康心理学中的半自动证据综合：当前方法和未来前景。

健康心理学的证据基础广泛且发展迅速。这些因素使得在就给定现象的知识状态做出决策时（例如，变量的关联，干预对特定结果的影响），很难（有时实际上是不可能）考虑所有可用证据。通过对给定研究领域的知识进行简要，可行的总结，对研究进行系统的综述，荟萃分析和其他严格的综合方法，可以缓解该问题。然而，由于新证据的迅速积累，进行这些合成变得越来越费力。现有的人工合成方法无法很好地扩展。在这篇文章中，我们讨论了通过机器学习和自然语言处理方法进行的半自动化如何帮助研究人员和从业人员更有效地审查证据。我们概述了健康心理学中的具体示例，重点介绍了目前可用的实用开放源代码技术。我们指出了更高级方法的潜力，并讨论了如何避免自动审阅的陷阱。