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Assessing Differences in Attitudes toward Occupational Safety and Health Measures for Infection Control between Office and Assembly Line Employees during the COVID-19 Pandemic in Germany: A Cross-Sectional Analysis of Baseline Data from a Repeated Employee Survey

Author

Listed:
  • Jana Soeder

    (Institute of Occupational and Social Medicine and Health Services Research, University Hospital Tübingen, University Tübingen, Wilhelmstraße 27, 72074 Tübingen, Germany)

  • Anna T. Neunhöffer

    (Institute of Occupational and Social Medicine and Health Services Research, University Hospital Tübingen, University Tübingen, Wilhelmstraße 27, 72074 Tübingen, Germany)

  • Anke Wagner

    (Institute of Occupational and Social Medicine and Health Services Research, University Hospital Tübingen, University Tübingen, Wilhelmstraße 27, 72074 Tübingen, Germany)

  • Christine Preiser

    (Institute of Occupational and Social Medicine and Health Services Research, University Hospital Tübingen, University Tübingen, Wilhelmstraße 27, 72074 Tübingen, Germany)

  • Benjamin Rebholz

    (Institute of Occupational and Social Medicine and Health Services Research, University Hospital Tübingen, University Tübingen, Wilhelmstraße 27, 72074 Tübingen, Germany)

  • Diego Montano

    (Department of Population-Based Medicine, Institute of Health Sciences, University Hospital Tübingen, Hoppe-Seyler-Straße 9, 72076 Tübingen, Germany)

  • Norbert Schmitz

    (Department of Population-Based Medicine, Institute of Health Sciences, University Hospital Tübingen, Hoppe-Seyler-Straße 9, 72076 Tübingen, Germany)

  • Johanna Kauderer

    (Medical Services, Robert Bosch GmbH, P.O. Box 10 60 50, 70049 Stuttgart, Germany)

  • Falko Papenfuss

    (Medical Services, Robert Bosch GmbH, P.O. Box 10 60 50, 70049 Stuttgart, Germany)

  • Antje Klink

    (Medical Services, Robert Bosch GmbH, P.O. Box 10 60 50, 70049 Stuttgart, Germany)

  • Karina Alsyte

    (Medical Services, Robert Bosch GmbH, P.O. Box 10 60 50, 70049 Stuttgart, Germany)

  • Monika A. Rieger

    (Institute of Occupational and Social Medicine and Health Services Research, University Hospital Tübingen, University Tübingen, Wilhelmstraße 27, 72074 Tübingen, Germany)

  • Esther Rind

    (Institute of Occupational and Social Medicine and Health Services Research, University Hospital Tübingen, University Tübingen, Wilhelmstraße 27, 72074 Tübingen, Germany)

Abstract

In our study, we investigated possible differences across occupational groups regarding employees’ perceived work-related risk of infection with SARS-CoV-2, attitudes toward technical, organisational, and personal occupational safety and health (OSH) measures for infection control, and factors associated with this attitude. We analysed baseline data (10 August to 25 October 2020) from a repeated standardised online survey distributed at a worldwide leading global supplier of technology and services in Germany. 2144 employees (32.4% women; age (mean ± SD): 44 ± 11 years) who worked predominantly remotely ( n = 358), at an on-site office ( n = 1451), and assembly line/manufacturing ( n = 335) were included. The work-related SARS-CoV-2 risk of infection differed between office employees working remotely and on-site (mean ± SD = 2.9 ± 1.5 vs. 3.2 ± 1.5; Mann-Whitney-U-Test: W = 283,346; p < 0.002; ε 2 = 0.01) and between on-site office and assembly line/manufacturing employees (3.8 ± 1.7; W = 289,174; p < 0.001; ε 2 = 0.02). Attitude scores toward technical OSH-measures differed between remote and on-site office (4.3 ± 0.5 vs. 4.1 ± 0.6; W = 216,787; p < 0.001; ε 2 = 0.01), and between on-site office and assembly line/manufacturing employees (3.6 ± 0.9; W = 149,881; p < 0.001; ε 2 = 0.07). Findings were similar for organisational and personal measures. Affective risk perception, COVID-19-specific resilience, and information about COVID-19-related risks were associated with the employees’ attitudes. To promote positive attitudes, it seems to be important to consider occupational-group-specific context factors when implementing OSH-measures for infection control.

Suggested Citation

  • Jana Soeder & Anna T. Neunhöffer & Anke Wagner & Christine Preiser & Benjamin Rebholz & Diego Montano & Norbert Schmitz & Johanna Kauderer & Falko Papenfuss & Antje Klink & Karina Alsyte & Monika A. R, 2022. "Assessing Differences in Attitudes toward Occupational Safety and Health Measures for Infection Control between Office and Assembly Line Employees during the COVID-19 Pandemic in Germany: A Cross-Sect," IJERPH, MDPI, vol. 20(1), pages 1-18, December.
  • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:614-:d:1019492
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    References listed on IDEAS

    as
    1. Bonin, Holger & Rinne, Ulf, 2021. "Arbeitssituation und Belastungsempfinden im Kontext der Corona-Pandemie im Juli 2021," IZA Research Reports 121, Institute of Labor Economics (IZA).
    2. Anke Wagner & Ladina Schöne & Monika A. Rieger, 2020. "Determinants of Occupational Safety Culture in Hospitals and other Workplaces—Results from an Integrative Literature Review," IJERPH, MDPI, vol. 17(18), pages 1-23, September.
    3. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    4. Bonin, Holger & Krause-Pilatus, Annabelle & Rinne, Ulf, 2021. "Arbeitssituation und Belastungsempfinden im Kontext der Corona-Pandemie im August 2021," IZA Research Reports 122, Institute of Labor Economics (IZA).
    5. Bonin, Holger & Krause-Pilatus, Annabelle & Rinne, Ulf, 2021. "Arbeitssituation und Belastungsempfinden im Kontext der Corona-Pandemie im September 2021," IZA Research Reports 125, Institute of Labor Economics (IZA).
    6. Bonin, Holger & Krause-Pilatus, Annabelle & Rinne, Ulf, 2021. "Arbeitssituation und Belastungsempfinden im Kontext der Corona-Pandemie im April 2021," IZA Research Reports 116, Institute of Labor Economics (IZA).
    7. Bonin, Holger & Krause-Pilatus, Annabelle & Rinne, Ulf, 2021. "Arbeitssituation und Belastungsempfinden im Kontext der Corona-Pandemie im März 2021," IZA Research Reports 114, Institute of Labor Economics (IZA).
    8. Bonin, Holger & Krause-Pilatus, Annabelle & Rinne, Ulf, 2021. "Arbeitssituation und Belastungsempfinden im Kontext der Corona-Pandemie," IZA Research Reports 108, Institute of Labor Economics (IZA).
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