The COVID-19 pandemic has generated global interest in understanding its psychological and occupational impacts. The COVISTRESS project represents a major international initiative for collecting and analyzing data on perceived stress during and after the pandemic, with a focus on occupational factors. This study provides a bibliometric and methodological review of the COVISTRESS research dataset, examining publication patterns, collaboration networks, thematic trends, and statistical techniques employed in associated studies. Findings indicate that while COVISTRESS successfully captured diverse experiences across multiple countries and offered a foundational dataset, the scientific impact of COVISTRESS publications has been modest relative to non-COVISTRESS research on COVID-19 stress. Most studies relied on traditional survey designs and classical statistical methods, limiting longitudinal and cross-cultural comparability. Citation trends suggest that more computationally advanced and high-dimensional approaches achieve greater visibility. These insights underscore the potential for future methodological renewal, including open-access dissemination, longitudinal designs, and machine learning approaches, to enhance both academic influence and data-driven strategies for occupational stress management during public health crises.
Rabbouch et al. (Fri,) studied this question.