Assessing Occupational Stress During COVID-19: Bibliometric and Methodological Insights from the COVISTRESS Corpus

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 data set, 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 data set, 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.

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Publication Details

Journal
Advances in Data Science and Adaptive Analysis
Published
2026-04-06
DOI
https://doi.org/10.1142/s2424922x26500075
Primary Topic
COVID-19 and Mental Health
Type
article
Field-Weighted Citation Impact
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article

Assessing Occupational Stress During COVID-19: Bibliometric and Methodological Insights from the COVISTRESS Corpus

Foued Saâdaoui, Hana Rabbouch, Frederic Dutheil
Advances in Data Science and Adaptive Analysis
COVID-19 and Mental Health
article

Assessing Occupational Stress During COVID-19: Bibliometric and Methodological Insights from the COVISTRESS Corpus

Foued Saâdaoui, Hana Rabbouch, Frederic Dutheil
article en

Abstract

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 data set, 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 data set, 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.

Advances in Data Science and Adaptive Analysis
Centre National de la Recherche Scientifique (FR), Higher Institute of Management (RU), University of Clermont Auvergne (FR), International University of Rabat (MA), University of Sousse (TN)
Openalex Percentile: Top 21%
COVID-19 and Mental Health
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