Summarising Literature on Mental Health in Academia: Machine Learning Methods and Human Expertise
Creators
- 1. TIB - Leibniz Information Centre for Science and Technology
Description
Studying mental health in academic environment is a complicated topic, which was for a long time underrepresented in literature (Gurtie et al., 2017). In recent years, research findings have been accumulated in the area (Mattijssen et al., 2021), and their number is growing. The findings were summarized in systematic reviews, such as Sabagh et al. (2018) on faculty burnout, Hazell et al. (2020) on mental health of doctoral researchers, or Salimyedeh et al. (2021) on coping with stress in academia. However, this traditional approach deals with rather limited samples of publications: for instance, 36 papers in Sabagh et al. (2018), 22 papers in Hazell et al. (2020), and 52 papers in Salimyedeh et al. (2021). In this study, we rely upon advanced machine learning techniques that make it possible to obtain a wider picture of the area on larger samples of literature. Our task is to illustrate how machine learning methods and human expertise complement each other in summarizing literature on mental health in academia.
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References
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