Grey literature: An important resource in systematic reviews.

By A. Paez. Journal of Evidence-Based Medicine, 2017

1,331 citations recorded by the source corpus.

Subfields: History and Philosophy of Science, Public Health, Environmental and Occupational Health, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f76b64db1500b93b56beac8e65b2e0c191fef552

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Isabela Albuquerque
2. Alexandre Gramfort
3. Yannick Roy
4. Hubert J. Banville
5. Tiago H. Falk
6. Jocelyn Faubert

Citations in the corpus

• A critical analysis of the impacts of COVID-19 on the global economy and ecosystems and opportunities for circular economy strategies (2020, neutral): “However, a number of authors (Morrison et al., 2012; Paez, 2017) have reported that SLR does not allow for effective synthesis of academic and grey literature which are not indexed in popular academic search engines like Google Scholar, Web-of-Science and Scopus.”
• Deep learning-based electroencephalography analysis: a systematic review (2019, background): “Moreover, unconventional research ideas are more likely to be shared in such repositories, which improves the diversity of the reviewed work and reduces the bias possibly introduced by the peer-review process [126].”
• Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review (2023, background): “A limitation of our study is that we did not consider grey literature, which consists of academic papers such as theses and essays, research and committee reports, official reports, conference articles, and ongoing research [161].”